Prove the AI Use Case. Measure the Tokens. Budget With Confidence.
The AI Fusion Lab is a secure, time-boxed sandbox in AWS GovCloud where your agency can rapidly test a generative AI use case, compare models side-by-side, and instrument real token consumption — so you walk away with an accurate budget for ongoing production operations before you commit a dollar.
De-Risk AI Adoption Before You Scale
Most AI programs stall at the gap between a promising demo and a defensible production budget. The AI Fusion Lab closes that gap, and our real innovation is the platform automation behind it: we rapidly stand up a secure, instrumented environment, let you prove the approach and measure the numbers, then rapidly tear it down — all inside a FedRAMP High AWS GovCloud boundary.
Stand Up Fast, Tear Down Fast
Our platform automation provisions a secure lab in days and decommissions it just as quickly when you're done — so you pay for capability, not idle infrastructure.
Measure the Tokens
Instrument actual token consumption per use case to model accurate, defensible costs for ongoing operations.
Compare Models
Run open-source and licensed models against identical prompts and data, then decide on accuracy, latency, and cost.
Secure by Inheritance
The lab is built in AWS GovCloud and operated by GovDataHosting, inheriting FedRAMP High controls from day one.
A Sandbox With a Start and an End
The AI Fusion Lab is intentionally temporary. A typical engagement runs anywhere from a few weeks to a few months — long enough to prove the use case and gather real operational data, short enough to keep cost and scope tightly contained.
It is a neutral place to experiment safely, separate from your production systems. When the evaluation concludes, you either retire the environment cleanly or promote the winning approach into a hardened production enclave with stricter controls.
No long-term lock-in, no standing infrastructure cost between pilots. GovDataHosting's platform automation lets us spin the lab up rapidly and tear it down just as rapidly — you get exactly the environment you need, for exactly as long as you need it, and not a day more.
The Lab in One Sentence
Evaluate approaches before you commit.- Time-boxed: weeks to months, with a defined scope and exit.
- Isolated: a dedicated enclave separate from production.
- Instrumented: token and cost telemetry captured throughout.
- Promotable: a clear path from pilot to production ATO.
- Fully managed: operated 24×7 by a U.S. team in U.S. cloud datacenters.
- Cost: low or none — eligible engagements may qualify for no-cost cloud consumption.
From Tokens to a Defensible Budget
A demo tells you whether AI can solve the problem. The AI Fusion Lab tells you what it will cost to run — at your real volumes, on your real data — so you can fund it with confidence.
Every prompt and response in the lab is metered. We capture input and output tokens per use case, per model, and per workflow, then translate that consumption into projected monthly and annual operating costs at the volumes you actually expect in production.
- 1Run representative workloads against candidate models in the lab.
- 2Capture per-request input/output token counts and latency.
- 3Multiply measured usage by your projected production volume.
- 4Add infrastructure, storage, and support to model total cost.
- 5Deliver a budget estimate your ATO and finance teams can defend.
Figures shown are placeholders to illustrate the report format. Actual token counts, latency, and projected run-rate are derived from your own workloads during the engagement and delivered in your decision package.
Pilot Safely, Decide Confidently
A repeatable path from a raw idea to an authorization-ready decision — with the data to back every choice.
Intake
Load representative, unclassified or approved datasets into an isolated enclave.
Compare
Run candidate models against identical prompts and guardrails.
Ground
Add retrieval over your corpus; measure factuality, accuracy, and drift.
Measure & Decide
Weigh accuracy, latency, and token cost to select the best fit.
Promote
Migrate the winning artifacts into a production enclave with stricter controls.
Built for Real Evaluation, Not Just Demos
The capabilities you need to test rigorously and document defensibly — inside one secure AWS GovCloud boundary.
Evaluate & Select Models
- Open-source and licensed models, side-by-side
- Ground responses with retrieval and vector search
- Capture accuracy, latency, and cost metrics
Train, Fine-Tune & Optimize
- Run fine-tuning jobs in a dedicated enclave
- Version weights, embeddings, and prompts
- Right-size GPU tiers; autoscale for spikes
Security & Guardrails
- RBAC, private endpoints, immutable audit logs
- PII/PHI scrubbing and safe-logging options
- Egress controls to block prompt-injection & exfiltration
Cost & Token Telemetry
- Per-request input/output token accounting
- Run-rate projection to production volumes
- Model-by-model cost comparison dashboard
Compliance & ATO Support
- Controls mapped to NIST 800-53 Rev 5 families
- Documentation, test evidence, ConMon outputs
- Alignment with FedRAMP / FISMA expectations
AI Governance
- Aligned to NIST AI RMF functions: Govern, Map, Measure, Manage
- Supports governance needs under OMB M-25-21
- Clear roles for model owners, data stewards, AOs
Concrete Deliverables, Not Just Impressions
Every engagement concludes with a decision package your team can take straight into procurement and authorization.
Method of Evaluation
The test plan, datasets, prompts, and guardrails used — so results are reproducible and defensible.
Metrics & Cost Dashboard
Accuracy, latency, and token consumption per model, with a projected production run-rate.
Decision Memo
A clear recommendation on model and architecture, justified by the measured evidence.
Authorization-Ready Plan
A deployment plan and budget estimate aligned to your A&A process and ready to scope.
Built for the People Who Have to Defend the Decision
Federal, State & Local Agencies
Agencies that need to test and refine foundation models on real data before committing to a production AI investment.
Primes & Subcontractors
Contractors collaborating with agencies under strict data controls who need a compliant place to prove an AI capability.
Program Offices, CISOs & ATO Owners
Leaders seeking a practical, evidence-backed path from pilot to production — with auditable controls and a real budget.
Scoped, Flexible, and Teaming-Friendly
The lab is sized to your use case and procured through the contract vehicle that fits your agency.
Automated Stand-Up & Tear-Down
Platform automation gets pilots running within days once security prerequisites are met. The engagement runs a few weeks to a few months, then the environment is decommissioned cleanly on demand — no idle infrastructure left behind.
Consumption-Based in GovCloud
Built on AWS GovCloud with consumption-based compute, storage, and a fixed managed-support fee — you pay for the resources the pilot actually uses, with no long-term lock-in. Eligible engagements may qualify for AWS funding that offsets, and in some cases fully covers, cloud consumption.
Available Through Your Vehicle
Available for agency and contractor procurements and teaming arrangements. Include support tier and environment size in your RFP/RFQ for an accurate quote.
Eligible Customers May Pay Nothing for Cloud Consumption
Many government AI pilots qualify for AWS funding that can offset — and in some cases fully cover — the lab's cloud consumption for up to one to two months. Eligibility depends on your use case and procurement, so a short conversation tells you whether yours qualifies.
Where the Lab Leads Next
AI Fusion Lab, Answered
What exactly is the AI Fusion Lab?
It is a secure, time-boxed sandbox built in AWS GovCloud where you evaluate one or more AI use cases — comparing models, grounding them on your data, and measuring real performance and token cost — before committing to a production deployment.
How long does an engagement last?
The lab is temporary by design. A typical engagement runs anywhere from a few weeks to a few months — long enough to gather meaningful operational data, short enough to keep scope and cost tightly contained.
How do you estimate ongoing operating costs?
We meter input and output tokens for each model and workflow during the pilot, then project that consumption to your expected production volumes and layer in infrastructure, storage, and support — producing a budget estimate your finance and ATO teams can defend.
Can we compare different models before choosing one?
Yes. The lab runs open-source and licensed models against identical prompts, datasets, and guardrails, so you can choose based on measured accuracy, latency, and cost rather than vendor claims.
How fast can you provision the environment?
Pilots can typically begin within days after security prerequisites are met. Promotion to a production environment follows a standard build book with repeatable timelines, shared during scoping.
What is the path from pilot to ATO?
Pilot in the lab, capture metrics and baselines, then promote the winning approach to a production enclave with hardened images, formal change control, and a documentation set aligned to your assessment and authorization process.
How is the lab kept secure?
It runs inside an AWS GovCloud FedRAMP High boundary with RBAC, network isolation, encryption, immutable audit logging, content filters, and egress controls on inference endpoints — operated by U.S. citizens in U.S. cloud datacenters.
What happens to data and artifacts when the lab ends?
At the conclusion of a time-boxed engagement you either retire the environment cleanly per agreed data-handling rules, or promote approved artifacts into a production enclave with stricter controls. Nothing is left running by default.
Can the pilot be no-cost for our agency?
Potentially. Many government AI pilots qualify for AWS funding that can offset — and in some cases fully cover — the lab's cloud consumption for up to one to two months. Eligibility depends on your use case and procurement, so a short conversation determines whether yours qualifies. Funding is subject to eligibility and AWS approval.
What kinds of AI use cases is the lab good for?
Common pilots include document summarization, retrieval-augmented search over an agency corpus, chat assistants, classification, and data extraction. If a use case depends on sensitive data or needs measured cost before a production commitment, it's a strong fit. We'll help scope whether yours is a good candidate during the intake conversation.
Ready to Scope Your AI Pilot?
Tell us your use case. We'll stand up a secure AI Fusion Lab in AWS GovCloud, prove what works, and hand you the token-level budget to fund it for the long run.