Hosting: Private Weights: Audited External API traffic: 0 bytes

// DELTA TANGO AI ADVISORY

Custom AI models.
Verified at the source.
Run inside your network.

We design, train and deploy private language models and AI agents engineered to act as a force multiplier for human capability. Our sovereign systems keep data strictly in-house, amplify institutional expertise, and preserve human accountability at every step.

SEE THE MODELS COMPARE THE RISKS

Planning AI for a bank, a government department, or a national enterprise? See bespoke enterprise builds

01 // DATA CUSTODY

100% In-House

Models run on dedicated hardware at your site, under your control.

02 // WEIGHT PROVENANCE

Audited Weights

Built from verified base models, not unvetted community downloads.

03 // RESPONSE SPEED

50-75 tok/sec

Per user on a compact model, running on your own hardware.

04 // TOKEN ECONOMICS

Zero Per-Token Fees

You own the models and the runtime, so the bill does not grow with every query. What is a token?

// PRIVATE INFERENCE ENGINE

[KERNEL] Starting private runtime...

[VERIFY] Model SHA256 checksum matches the trusted source.

[SECURITY] Backdoor and trigger scan of model weights: no known signatures found.

[EGRESS] Outbound telemetry blocked at the network boundary.

"Our philosophy is simple: AI is an amplifier, not a replacement. We build custom sovereign systems that take on the heavy cognitive lift of data synthesis, document correlation, and routine execution, acting as a force multiplier so your people can focus their expertise where judgement matters most."

[ACTIVE MODEL] Delta Babylonia Omni 25B (mixture-of-experts, 4-bit).

[THROUGHPUT] Decode rate: 52.4 tokens/sec. Time to first token: 180ms.

// THE RISKS

Three ways enterprise AI goes wrong.

Most organisations adopting AI run into one of three problems. Their data leaves their control through cloud APIs. They deploy community models nobody has vetted. Or they connect agents to live web search, where hidden instructions on a web page can redirect them (known as indirect prompt injection).

RISK 01 // SHARED PUBLIC CLOUD

Your data leaves the building

Prompts, contracts and operational records are processed on infrastructure shared with other customers. Once sent, they cannot be recalled, which can put you in breach of privacy or contractual obligations.

Models change without notice

Providers update models and moderation rules on their own schedule. A workflow that worked last month can start giving different answers.

Costs grow with use

Per-token billing rises as more staff use the tool, so wider adoption means a bigger bill every month.

RISK 02 // UNVERIFIED PUBLIC MODELS

Hidden backdoors in model weights

Anyone who publishes a model can train in a hidden trigger that changes its behaviour. Antivirus tools and code scanners do not inspect model weights, so these backdoors are hard to detect.

Triggered data theft

If an agent reads an email or document containing the trigger phrase, a compromised model can quietly call a tool and send out passwords or keys.

Supply-chain risk

Downloading models from public hubs brings unvetted files into core systems, with no reliable record of who built them or how.

RISK 03 // LIVE WEB SEARCH

Poisoned web pages

Attackers hide instructions in web pages. When a model reads that text during a search, it can be steered into running tools it should not, or leaking data.

Search queries reveal your plans

Agents connected to commercial search engines send their queries to outside providers. Those queries can reveal strategy, litigation research or acquisition targets.

Unreliable sources

Search results change constantly. Manipulated rankings, altered pages and hijacked domains can feed bad information into automated decisions.

THE DELTA TANGO APPROACH

Custom fine-tuning. Verified sources. Offline runtimes.

We address all three risks. We fine-tune your models from verified base models, give them an offline knowledge library instead of public web search, and block unauthorised outbound traffic at the network level.

// THREAT SIMULATOR

See how each approach handles an attack.

Pick a threat below to see how a public cloud service or an unvetted community model compares with a private Delta Tango deployment.

Public cloud and community models CRITICAL RISK

A tampered training dataset plants a hidden trigger phrase in a public model. When an agent later reads an email containing that phrase, the model makes an unauthorised tool call and sends internal API keys and passwords out of the network.

Data custody 15%

Supply-chain assurance 20%

Delta Tango private deployment 100% AIR-GAPPED

We check where every base model came from before we build on it. Models run in isolated containers, and outbound traffic is filtered at the network level, so an attempt to send data out is blocked.

Data custody 100%

Supply-chain assurance 98%

// BEYOND THE REFERENCE BUILDS

Bespoke enterprise models, built at the scale of your organisation.

The series above shows what a private model can do on a workstation, a server, or a rack. Banks, government departments, and national enterprises need more: models that understand the whole organisation, serve thousands of people at once, and keep an entire matter, portfolio, or code base in view. For these clients we design a bespoke model and the platform it runs on.

1T+ Parameter class Trillion-parameter-class open-weight foundations. Only a fraction of the parameters work on each token, so responses stay quick.

1M+ Tokens of context Long-context architectures combined with private search, so a loan book, a regulatory library, or a whole repository can be in view.

10,000+ People supported Capacity planned around how many people and agents work at once, with room to grow as adoption spreads.

2+ sites Resilient by design Multi-node GPU clusters in your data centre or an Australian sovereign facility, with failover between sites.

// WHAT IT CAN LOOK LIKE

A major Australian bank

Risk, legal, technology, and customer operations

Foundation. A trillion-parameter-class open-weight model, given continued training on the bank's policies, product terms, procedures, and code under strict data controls.
Specialists. Adapters for credit risk, financial crime, regulatory reporting, and engineering, each measured against the bank's own test sets.
Platform. Several 8-GPU data-centre nodes across two sites, with private search over millions of documents and full audit logging.
Governance. Model risk management aligned to APRA CPS 230 and CPS 234, human sign-off on material decisions, and a regular evaluation and red-team cycle.

Illustrative only. Every programme is scoped in discovery around your data, people, and obligations.

01 Discovery and business case 4 to 6 weeks. Use cases, data readiness, risk appetite, sizing, and a costed plan your board can approve.
02 Pilot on your data 12 to 16 weeks. A working model on a contained platform, measured against agreed accuracy and safety targets.
03 Scale and operate 6 to 12 months. A staged rollout across business units, with training, monitoring, and continuous improvement.

Planning AI across a large organisation? See an indicative enterprise footprint in the configurator, or talk it through with a senior practice lead.

Configure an enterprise build Request a briefing

// AI AGENTS

AI agents that augment your team, with safety controls built in.

Language models become a genuine force multiplier when they can work alongside your people: querying internal systems, correlating complex records, and executing multi-step tasks in isolated environments. We build AI agents that augment human workflows while keeping your professionals in command of every decision.

Isolated containers

Each agent runs in its own container without administrator rights and with fixed memory limits. This limits how far a compromised agent can reach into the rest of your systems.

CONTAINER NAMESPACE ISOLATION

Outbound traffic controls

Each container's network traffic is filtered at the operating system level. Agents can only connect to approved internal services and authenticated endpoints; anything else is blocked.

NFTABLES EGRESS FILTERING

Checked tool calls

Before an agent's tool call runs, it is checked against a strict format (a JSON schema). Malformed calls, unexpected parameters or unusual sequences are caught and stopped.

STRICT JSON SCHEMAS

No inbound connections

Your AI servers connect out through encrypted tunnels only. There are no open inbound ports, no public IP addresses and no services listening for outside connections.

OUTBOUND-ONLY TUNNELS

Offline knowledge libraries

Instead of searching the live web, agents draw on read-only reference libraries stored on fast local drives. Technical material, standards and internal archives return results in sub-10ms, with no outbound internet traffic.

READ-ONLY OFFLINE LIBRARIES

Private document search with filtering

Document search runs on your own hardware. Before retrieved passages reach the model, they are filtered to remove hidden instructions, injection attempts and executable code.

FILTERED PRIVATE RETRIEVAL

// YOUR EXISTING WORKSPACE

Make your existing cloud workspace safer and more capable.

Most regulated organisations already run a cloud workspace, a central sign-in system and a desktop office suite. We do not ask you to replace them. We turn your existing workspace into an augmented productivity engine: fixing permission gaps, switching off public web leakage, and giving your professionals private models that act as an everyday force multiplier across documents, spreadsheets and communication.

PART 01 // SECURITY

SECURITY UPLIFT

Secure the AI already in your workspace

We close four common security and compliance gaps in default workspace AI set-ups.

UPLIFT 01

Fix oversharing before the AI finds it

Old, overly broad file permissions become a problem once an AI assistant can search everything. We tidy permissions, apply sensitivity labels, and limit assistants to approved libraries for each role, so payroll, executive pay and client files stay with the people who should see them.

UPLIFT 02

Switch off live web search

We disconnect commercial search engines from workspace assistants. This removes the main route for indirect prompt injection and malicious web content, and stops your prompts ending up in outside search logs.

UPLIFT 03

Keep sensitive material out of cloud indexes

High-value material such as archives, bids, source code and compliance registers is indexed on private local storage instead of a shared cloud index. Retrieved content is filtered for hidden instructions before the model sees it.

UPLIFT 04

Keep data onshore and out of training

We put contractual Zero Data Retention (ZDR) terms in place and restrict processing to onshore cloud regions. Your inputs, temporary data and outputs are contractually excluded from model training.

PART 02 // CAPABILITY

CAPABILITY ENHANCEMENT

Connect your workspace to private models

Give your existing office suite specialised models, unmetered usage and hardware sized to your team.

ENHANCE 01

Works inside the tools staff already use

Staff use AI inside their usual documents, spreadsheets, email and chat. There is no separate portal and no extra login, which makes adoption much easier.

ENHANCE 02

Private models behind your sign-in

Complex requests pass through gateways that check your organisation's sign-in, then on to private Delta Babylonia models sized for the work, from compact models on a workstation to bespoke enterprise builds. These are tuned to your field in ways a general cloud model is not.

ENHANCE 03

Hardware sized to your team

We size the infrastructure to how many people use it at once: from adding a graphics card to an existing workstation (about $2,000 to $10,000) for a small team, to a 192GB professional workstation ($62,000 to $80,000) for a department, to rack servers ($120,000 and up) for 500 to 2,000 users, and multi-node enterprise platforms for organisation-wide use. See hardware tiers →

ENHANCE 04

What happens when usage is not rationed (case study)

You keep your existing software investment and stop rationing AI use to control costs. In the industry case study below, a financial institution with 2,000 engineers reported a 90% reduction in inference costs (~$0.30/M tokens), 60% less time spent coding, and 70% faster delivery lead times.

// HOW IT FITS TOGETHER

How a request flows

Staff work in familiar tools; the processing happens on isolated private hardware.

01

Your workspace

Staff ask questions or start tasks from chat, documents or spreadsheets.

Familiar tools

02

Sign-in checks

Your identity system checks multi-factor sign-in (FIDO2), device compliance, location and sensitivity labels.

Zero Trust access

03

Private Gateway

A secured gateway (OAuth) sends the request to your private hardware rather than a shared cloud service.

Authenticated access

04

Private compute

Custom Babylonia models run on private GPU hardware and search an offline knowledge library on local drives.

100% Private Execution

05

Checked response

The filtered, logged response appears back in the user's document.

Logged and auditable

// GOVERNANCE AND COMPLIANCE

Private AI designed around the frameworks you report against.

Shared cloud AI services add outside dependencies, give you limited visibility of how data is handled, and can change their outputs without notice. All of this makes compliance harder. We design private AI infrastructure to meet the controls in the Australian Signals Directorate (ASD) Essential Eight, the NIST AI Risk Management Framework, and ISO/IEC 42001 and 27001, along with obligations under APRA CPS 230 (Operational Resilience), the SOCI Act, the Australian Privacy Principles (APP 8), GDPR and the EU AI Act.

ACSC / ASD ESSENTIAL EIGHT

DESIGNED FOR ML3

Maturity Level 3 controls for AI

Run AI agents and local models without weakening Australia's baseline cyber security controls.

CONTROL 01 Application control for agents

Agent tool calls and generated code run inside signed containers. Unapproved programs, scripts and shell commands are blocked by default.

CONTROL 02 Restricted administrative privileges

Models and agents run under separate service accounts without administrator rights. They do not inherit admin credentials or have access to privileged host functions, and their memory is isolated, which limits lateral movement.

CONTROL 03 User application hardening and offline knowledge

Uncontrolled web search is replaced with read-only offline libraries and search indexes. This removes the main routes for remote prompt injection, malicious web content and harmful document macros.

CONTROL 04 Regular immutable backups and model provenance

Model weights, training checkpoints and search indexes are versioned, hashed and backed up to read-only offline storage, so you can show auditors what was deployed and recover quickly after an incident.

NIST AI RMF 1.0

FULL LIFECYCLE

NIST AI Risk Management Framework

Structured risk management at each stage of the AI lifecycle, so systems stay reliable, explainable and resilient.

NIST: GOVERN Accountability and oversight

Clear policies that set operating limits, define when a person must review an AI decision, and assign accountability for automated decisions.

NIST: MAP Risk profiling in context

We map organisational dependencies, attack surfaces, model limitations and downstream business impacts for each fine-tuned model and agent workflow.

NIST: MEASURE Measurement and weight provenance

Automated benchmarks track hallucination rates, bias drift, resistance to adversarial inputs and the integrity of model weights over time.

NIST: MANAGE Input filtering and isolation

Live input filtering, prompt injection defences and hardware kill switches can isolate an agent that behaves unexpectedly before it changes anything.

ISO/IEC 42001

SUPPORTS AN AIMS

AI management system

The first international standard for AI management systems, covering governance, quality assurance and responsible deployment.

CLAUSE 6 & 8 AI lifecycle management

Documented policies and procedures for dataset selection, fine-tuning controls, verification checkpoints, deployment and retirement.

ANNEX A.7 Data lineage and quality

We record where the data in your training sets and search indexes came from. This reduces the risk of third-party contamination, copyright exposure or unlicensed material.

ANNEX A.9 Reduced third-party dependency

Removes reliance on shared cloud APIs, where unannounced updates, changes to terms or outages can disrupt critical business processes.

ANNEX A.10 Impact assessment and incident records

Structured impact assessments, traceable reasoning logs and incident reporting procedures that stand up to board and regulator scrutiny.

ISO/IEC 27001 & PRIVACY ACT

EGRESS CONTROLLED

Information security and data sovereignty

Keeps intellectual property, strategic documents and customer data within your legal jurisdiction.

A.8.20 Network security and segregation

AI servers run on physically separate hardware or private virtual networks behind stateful firewalls. External telemetry and vendor callbacks are switched off.

A.8.24 Encryption throughout

Model weights, search indexes, working memory and temporary caches are encrypted at rest and in transit, using keys your organisation controls.

A.8.15 Signed audit logs

Detailed logs record model requests, agent plans, tool calls and security alerts, and are written to tamper-resistant, write-once storage.

APP 8 Australian Privacy Principles

Supports compliance with Australian cross-border disclosure rules: customer data and protected communications stay onshore rather than going to overseas cloud providers.

APRA CPS 230 & SOCI ACT

RESILIENCE FOCUSED

Operational resilience and critical infrastructure

Helps financial services and critical infrastructure operators stay within their tolerance levels when a cloud provider has an outage.

CPS 230: TOLERANCE Continuity of critical operations

Keeps automated claims processing, underwriting and treasury operations within defined tolerance levels. Because the models run on your premises, an outage at a cloud AI vendor does not stop your operations.

CPS 230: PROVIDERS Reduced material service provider risk

Reduces reliance on shared service providers whose unannounced model changes or one-sided changes to terms can make APRA's fourth-party oversight requirements hard to meet.

SOCI ACT Critical infrastructure isolation

Keeps operational technology controls, grid telemetry and telecommunications routing data away from overseas commercial AI services that may be open to foreign access.

DISASTER RECOVERY Local redundancy

Self-contained, air-gapped model servers support offline operational recovery, so core capabilities keep running during a major internet outage or cyber incident.

EU AI ACT & GDPR

HIGH-RISK SUPPORT

High-risk governance and accountability

Aligned with international rules for high-risk AI: explainable outputs, effective human oversight and verifiable records of who did what.

ARTICLE 14 Meaningful human oversight

AI augments human judgement rather than substituting for it. Models are tuned to question assumptions and provide verifiable evidence trails, ensuring that human delegates remain the ultimate decision-makers and accountability is never abdicated to a machine.

ARTICLES 11 & 12 Technical logging and traceability

Tamper-evident, hashed logs record model settings, prompts and tool calls, so results can be traced and reviewed during supervisory audits.

GDPR & APP 8 Cross-border privacy and data minimisation

Keeps personal records and confidential communications within their legal jurisdiction, with no cross-border transfer unless you approve it.

ARTICLE 15 Robustness, accuracy and cyber security

Regular adversarial testing against data poisoning, prompt injection and model drift, to keep accuracy consistent across your operations.

// SIDE-BY-SIDE COMPARISON

Shared cloud AI services compared with a private Delta Tango deployment

Compliance area	Shared cloud AI services	Private Delta Tango deployment
ASD Essential Eight Application Control & Privileges	LIMITED CONTROL Shared containers you cannot inspect; remote code runs outside your control; broad API permissions; no visibility of the host.	MATURITY LEVEL 3 Agents isolated in containers; signed code execution; service accounts without administrator rights.
Information Hardening Knowledge Retrieval & RAG	CRITICAL RISK Live web search exposes models to indirect prompt injection, hidden instructions and malicious macros.	OFFLINE BY DESIGN Read-only offline libraries on local drives; private document search; incoming content filtered before it reaches the model.
NIST AI RMF 1.0 Risk Mapping & Measurement	HARD TO VERIFY Closed proprietary weights; undisclosed training data; models can change without notice or documentation.	AUDITABLE Audited open weights (Delta Babylonia series); verified provenance; regular, repeatable testing.
ISO/IEC 42001 AI Management System	VENDOR LOCK-IN Vendor terms can change; data may be retained for training; API changes can break integrations without warning.	YOU OWN IT You own your fine-tuned weights and checkpoints; documented lifecycle and formal audit trails.
ISO/IEC 27001 & Privacy Data Sovereignty & Residency	JURISDICTION RISK Prompts may travel through overseas networks and be subject to the US CLOUD Act and other foreign disclosure laws.	STAYS IN AUSTRALIA No outbound telemetry; hosted on your premises or in a private Australian cloud; data stays under Australian law.

Ready for questions from regulators and the board

We work with CISOs, risk committees and technology executives to run AI risk audits, write AI governance policies, and deploy private language model infrastructure built to meet government and industry standards.

TALK TO US ABOUT GOVERNANCE

// COST OVER TIME

What cloud token billing costs over five years.

Cloud AI is billed per token, so the bill grows with every person and automated task that uses it. Compare five years of metered billing with owning a private deployment. New to tokens? Start here →

Step 01 // Daily token usage

10 Million Tokens / Day

• Cloud rate for leading reasoning models with long context: ~$18.50 AUD per million tokens, input and output blended.

• Private platform: a one-off cost for hardware and the model build, from about $120,000, rising with volume.

• Maintenance: ~12% a year for ongoing training updates and tuning.

CLOUD AI SERVICE

$0 Annual Token Spend

$0 5-Year Total Cost

An ongoing cost, and at the end your organisation owns no model or intellectual property.

DELTA TANGO PRIVATE DEPLOYMENT

$0 One-Off Model & Hardware Build

$0 5-Year Total (Build + Maintenance)

5-Year Net Saving: $0

Break-even in about 28 months

Want these numbers for your organisation? During an architecture review we build a costed business case from your actual usage, hardware, and growth plans.

Book an architecture review

// COMPARE YOUR OPTIONS

Compare three ways to deploy AI.

Each approach trades off speed to get started, compliance, day-to-day performance and long-term cost. Compare shared cloud APIs, dedicated cloud instances and self-hosted private deployments against the work you actually need done.

INDUSTRY CASE STUDY

10x COST SAVINGS

70% FASTER DELIVERY

Industry case study: ending token rationing

An industry example: a tier-one financial institution with over 2,000 software engineers moved from commercial cloud APIs to a private cluster of 56 dedicated enterprise GPUs running audited open-weight models. Without per-token billing, private AI became a genuine force multiplier: coding hours fell by 60%, delivery lead times improved by 70%, and developers no longer had to ration their usage, with all proprietary code kept safely onshore.

Read the full case study

// SCENARIO ESTIMATOR

Estimate the costs for your organisation

Choose your team size and monthly usage to compare ongoing cloud token costs with private hardware sized to match.

INTERACTIVE ESTIMATOR

1. NUMBER OF USERS

2. MONTHLY USAGE PER USER

ESTIMATED CLOUD COST

$11,000

Per month ($132,000 / year)

SUGGESTED PRIVATE HARDWARE

Workstation with two 96GB GPUs

Indicative Hardware: $62,000 – $80,000 AUD

PAYBACK PERIOD

~6.5 Months

Time to recover the hardware cost

PROJECTED 3-YEAR SAVINGS

$299,440 (76% saved)

Estimated net saving

Estimated total usage: 2,000 Million Tokens / Month

Cloud pricing varies: Commercial cloud API rates vary widely. Standard models and cached prompts start around $1.00 – $3.00 AUD per million tokens, while advanced reasoning models, large context windows (100k+ tokens) and vision-enabled agent loops range from $6.00 to $18.50+ AUD per million tokens. The cloud figures above use a blended rate ($5.50 AUD/M tokens) that reflects typical code generation, repository analysis and multi-turn agent work.

Hardware costs are indicative: Hardware costs depend on your set-up and do not rise in a straight line. Self-hosting does not have to mean a large upfront purchase. Adding a graphics card to an existing workstation can cost as little as $2,000 to $10,000. A 192GB professional workstation is about $62,000 to $80,000, rack servers start around $120,000, and multi-node enterprise platforms for organisation-wide use start from about $1.4 million. Figures include GST and exclude advisory, implementation, and build services.

OPTION 01 SHARED SERVICE

Shared Cloud APIs

Commercial models hosted by an outside provider and billed per token.

Cost / 1M Tokens: $3.00 – $15.00+

Usage Limits: Heavy Rationing

Data Custody: Shared Cloud

ASD Essential 8: Limited Control

OPTION 02 HOSTED DEDICATED

Dedicated Cloud Instance

Managed models running in a dedicated, private section of a large commercial cloud (a VPC).

Cost / 1M Tokens: $2.00 – $8.00

Usage Limits: Quota Limits

Data Custody: Shared Cloud Boundary

ASD Essential 8: Varies by provider

OPTION 03 • PRIVATE SELF-HOSTED

Delta Tango Private Deployment

Dedicated hardware on your premises or in a private hosted facility, running audited open-weight models you own.

Cost / 1M Tokens: ~$0.30 (90% Lower)

Usage Limits: No Rationing

Data Custody: Air-Gapped or Private

ASD Essential 8: Designed for Maturity Level 3

Capability	Shared Cloud APIs	Dedicated Cloud Instance	Delta Tango Private Deployment
Blended Token Cost Inference cost per 1M tokens	$3.00 – $15.00+ An ongoing variable cost that rises as more engineers use it, which often leads to token budget freezes.	$2.00 – $8.00 Fixed hourly server rates plus the cloud provider's management margin; costs are fairly predictable.	~$0.30 • 90% REDUCTION Costs are hardware depreciation and power, so each extra token costs very little and developers stop worrying about budgets.
Developer Speed & Delivery Impact on coding & release cycles	CONSTRAINED (+5-10%) When tokens are rationed, engineers hold back from large refactors or generating full test suites.	MODERATE (+15-20%) Useful for everyday coding tasks, but concurrency caps and queue limits cause occasional slowdowns.	60% LESS CODING TIME, 70% FASTER DELIVERY With unmetered internal use, all 2,000+ developers in the case study could work alongside an AI coding assistant throughout the day.
Code & Proprietary IP Custody Boundary protection & exfiltration	CRITICAL RISK Source code, business logic and credentials are sent over the internet to shared servers, often overseas.	SHARED BOUNDARY Data stays in your cloud account, but is still visible to provider telemetry and subject to foreign legal orders.	100% AIR-GAPPED CUSTODY Models run only on your premises or on dedicated private hardware, with no outbound data or external telemetry.
ASD Essential Eight Alignment Application control & non-root runtimes	LIMITED CONTROL Third-party containers you cannot inspect; tools run remotely outside your control; no visibility of the host system.	VARIES BY PROVIDER Cloud firewall rules and role-based access, but tools run on standard cloud servers without signed containers.	MATURITY LEVEL 3 ALIGNED Signed containers, service accounts without administrator rights, and an allow list of approved programs by default.
Knowledge Base & Document Search Company documents & injection defence	LIVE WEB EXPOSURE Live web search opens the door to indirect prompt injection, hidden instructions and malicious document macros.	CLOUD VECTOR DB Managed cloud search indexes with basic filtering; still needs an internet connection for the model and for indexing.	OFFLINE LOCAL STORAGE Read-only local libraries that respond in sub-10ms, audited search indexes, and filtering that strips injection attempts before content reaches the model.
Response Time & Consistency Worst-case (P99) response time & queueing	HIGH JITTER (2-12s) Shared public queues slow down at peak times, with rate-limit (429) errors and slower responses.	VARIABLE (800ms-3s) Dedicated instances are more consistent, but cold starts and virtualisation still cause some variation.	SUB-200MS CONSISTENT Dedicated hardware with local storage and GPUs gives low latency, no competition from other customers for capacity, and steady throughput.
Model Ownership & Fine-Tuning Weights, training provenance & IP	BLACK BOX / NO IP Closed weights; the vendor can change system prompts, reduce capabilities or retire models, and you have little recourse.	CONSTRAINED ADAPTERS Fine-tuning adapters (LoRA) are hosted on the cloud platform and stay tied to that provider.	FULL WEIGHT OWNERSHIP Your organisation owns its fine-tuned Delta Babylonia weights, checkpoints and provenance records. Base models stay under their open-weight licences.

Work out the right deployment for you

We assess technical readiness, size GPU hardware and audit model provenance, so your leadership team can decide whether, and how, to move from per-token cloud billing to a self-hosted deployment.

BOOK AN ARCHITECTURE REVIEW

// HOW WE WORK

Delivered in stages. No all-or-nothing commitment.

Every engagement starts with a confidential briefing with a senior practice lead. From there we break the work into clear stages. Each one delivers something useful on its own and ends with a decision point, so you only commit to the next step when you are ready.

STAGE 01 ADVISORY

Architecture and governance review

2 to 4 Weeks • Fixed Scope

A review of unapproved cloud AI use, token spending, data sovereignty risks, and readiness against the ASD Essential Eight and ISO standards.

Token spend and rationing impact audit
Open-weight model suitability roadmap
Hardware sizing and ROI business case
Compliance risk register

STAGE 02 PILOT

Pilot model and offline knowledge base

4 to 8 Weeks • Sprint Delivery

We build a working pilot in a test environment, fine-tune a Babylonia model on your data, and set up filtering for incoming content.

Domain fine-tuning on your material
Offline knowledge base on local storage
Prompt injection filters and guardrails
Accuracy benchmarking against your test sets

STAGE 03 PRODUCTION

Production build and ongoing support

8 to 16 Weeks • Then Ongoing Support

Full production rollout on dedicated or upgraded servers, with request routing across machines, monitoring, staff onboarding and quarterly tuning.

Host upgrade or dedicated bare-metal deployment
Zero-trust container sandboxing (ASD ML3)
Staff onboarding and prompt governance
Quarterly tuning and maintenance

STAGE 04 ENTERPRISE

Bespoke model and multi-site platform

6 to 12 Months • Staged Rollout

For large organisations: a bespoke enterprise model with continued training on your corpus, run on a resilient multi-node platform and rolled out business unit by business unit.

Bespoke 1T+ class model and specialist adapters
Multi-site GPU platform with failover
Model risk management and evaluation cycle
Steering committee reporting and adoption support

Start with a confidential briefing. Tell us what you are trying to achieve. We will come back with a view on where to start and what the first stage would involve.

Request a briefing

// OUR PRACTICE

Led by senior architects and engineers.

We believe technology should serve human agency. Our AI practice is led by experienced systems architects, machine learning engineers and security specialists who build sovereign AI that removes cognitive drag from complex operations, acting as a force multiplier for your executives, engineers and advisory teams while maintaining strict human accountability.

PRACTICE LEADERSHIP // AI SYSTEMS ARCHITECTURE

Custom fine-tuning of open-weight models

We build staged fine-tuning pipelines that teach open-weight models your organisation's knowledge, your procurement standards and the way your senior people reason through decisions.

Capability: Custom model design and low-rank adaptation (LoRA)

SECURITY AUDIT // MODEL WEIGHT FORENSICS

Backdoor and hidden trigger checks

We examine open-weight models and agent tool-calling paths for hidden backdoors and trigger phrases, and trace where the training data came from.

Capability: Model weight analysis and provenance verification

INFRASTRUCTURE // AIR-GAPPED DEPLOYMENTS

Private hardware clusters

We deploy on-premises GPU clusters, split large models across several machines, and run private AI at remote sites, with strict controls on outbound traffic.

Capability: Cluster management and private inference

0 Reference model families

0% Of in-house model weights audited for provenance

ZERO External data leaks recorded

1T+ Parameter class for bespoke builds

Talk to the people who would do the work. Your first conversation is with a senior practice lead, not a sales team.

Meet the team Request a briefing

// GET IN TOUCH

Let's talk about your AI plans.

Whether you want a model fine-tuned for your team, a backdoor check on models you already use, AI agents that run on an isolated network, or a bespoke enterprise model for a large organisation, we would like to hear about it.

What to expect

We work with executives, CTOs and security leaders who need firm control over their data and engineering they can verify. Every conversation is held in commercial confidence.

Senior people from the start Each engagement is led by senior practice leaders and principal machine learning engineers.

You own what we build Fine-tuned weights, adapters and deployment scripts belong to your organisation, with no licence lock-in.

Audit-ready documentation Each model comes with training records, data cleaning reports and security verification results.

A clear next step After the briefing you receive a short written summary of what we heard and a proposed scope for the first stage.

First name *

Last name *

Work email *

Phone

Organisation *

What can we help with? *

Tell us about your challenge *

Custom AI models.Verified at the source.Run inside your network.