Case  Study

Price-to-Win, Air-Gapped

Self-hosted estimation intelligence under NIST and CMMC; proposals from four weeks to five days.

Mid-size defence contractor and manufacturer

Client

Mid-size defence contractor and manufacturer

Market

Defence & advanced manufacturing

Scale

Complex hardware and software programmes

Duration

12+ months

Team

9 engineers

Engagement model

Managed Delivery

Delivery hub

London & Belgrade

Stack

Self-hosted Llama, Crew.ai agentic framework, RAG, Python, React

Capability

AI Engineering

Challenges

Budget overruns

Programmes consistently exceeded estimates by 20% to 40%.

Slow bidding

Manual proposal preparation took weeks, costing bids or winning them unprofitably.

Compliance constraint

Defence data required NIST CSF and CMMC alignment, with no external inference.

The solution

Air-gapped architecture

A fully self-hosted deployment on open-weight Llama models, with no external calls.

Agentic estimation

Specialised agents (hardware, software, risk) collaborating on each estimate.

Historical knowledge retrieval

RAG over past programmes and industry standards for grounded estimates.

Price-to-win support

Strategic recommendations against historical bid outcomes and market patterns.

5 days

Proposal Turnaround

Proposal preparation cut from around four weeks to five days.

35%

Accuracy Improvement

Cost estimates for complex hardware and software programmes made materially more precise.

60%

Budget Variance Reduction

The gap between initial estimate and final cost narrowed across programmes.

The honest section

What we would tell you before starting

Self-hosting cost more to run than a hosted API and the base model lagged the frontier on the hardest tasks. We benchmarked against a hosted baseline during scoping so the trade-off was a decision, not a surprise. For this compliance regime, self-hosting was the only viable answer.

Next step

Discuss a similar programme.

Tell us the outcome you need measured. We’ll be straight about what it takes.

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