Sovereign GenAI that ships to production, not to a slide deck
Most enterprise GenAI pilots die between the demo and the data centre. We engineer the other ninety percent — retrieval that doesn't hallucinate, fine-tunes that survive your audit, guardrails that hold under adversarial traffic, and air-gapped inference that meets the regulator on day one. Every engagement leaves behind running code, an evaluation harness, and a team that can extend it without us.
What we deliver
Production-grade RAG
Hybrid retrieval combining BM25 lexical search with dense vector embeddings, re-ranked by cross-encoders and grounded with inline citations to source paragraphs. We tune chunking strategies per document class, run answerability checks before generation, and cache embeddings to keep cost per query under a cent at enterprise volume.
Domain fine-tuning
SFT, DPO, and parameter-efficient LoRA adapters trained on your proprietary corpora — contracts, claims, clinical notes, code repositories. We curate the training data, run automated data-poisoning checks, version every checkpoint in MLflow, and deliver a model that consistently outperforms GPT-4 class baselines on your specific tasks at a fraction of the inference cost.
Sovereign LLM deployment
Llama 3.1, Mistral, Qwen, and Phi running on your hardware behind your firewall — no internet egress, no telemetry, no shared tenancy. Quantised to INT4 or AWQ for cost-efficient serving on commodity GPUs, with vLLM or TGI handling continuous batching. We have shipped this pattern inside central banks, defence agencies, and regulated insurers.
Agentic orchestration
Multi-step agents built on LangGraph or custom state machines with explicit tool registries, deterministic routing, and durable execution. Each agent ships with a replay log, cost budget, and human checkpoint policy — not the brittle ReAct loops that demo well and crash in week two of production.
Continuous evaluation
An eval harness wired into CI that runs LLM-as-judge, RAGAS faithfulness, regression suites, and adversarial red-team prompts on every commit. Production responses are sampled and scored daily; drift triggers an alert before a regulator does. You see the same dashboard the model owner sees.
Guardrails and safety
Layered defences: PII redaction via Presidio plus custom NER, prompt-injection classifiers, output schema validation, jailbreak detectors trained on your threat model, and rate-limited fall-throughs. We document the residual risk for your CISO and your auditor in the language they actually use.
Live GenAI completion
How a query actually flows.
A real trace through the sovereign stack. Six stages, ~1.4 seconds end-to-end, zero packets leaving your perimeter.
How we deliver
Discovery sprint
Two weeks with your business owner, data team, and compliance lead. We inventory your data, map regulatory constraints, define the success metrics that will live on the dashboard, and confirm the highest-ROI use case is also the most feasible one. You receive a written architecture proposal and a fixed-scope statement of work.
Architecture and ground truth
Senior engineers pick the right pattern — pure RAG, fine-tune, agent, or hybrid — and design the data pipeline, retrieval layer, evaluation harness, and serving stack. In parallel, subject-matter experts build a ground-truth set of two hundred to a thousand questions that will gate every subsequent change.
Build and harden
Six to nine weeks of focused engineering against the eval harness. Every PR runs the regression suite. We instrument tracing with LangSmith or OpenTelemetry, set up cost dashboards, run a red-team week, and complete the security review your CISO requires before production traffic touches it.
Controlled rollout
Shadow mode against historical traffic, then canary to one percent of live users, then a phased ramp with a documented rollback path. We sit in the war room for go-live and the first two weeks of hypercare.
Operate or transfer
You choose: MindMap continues to run the system under a managed-service SLA, or we transfer to your team with documentation, runbooks, and a four-week shadowing programme. Either way, the eval harness keeps running and the accuracy graph is yours to watch.
Generative AI across every sector
The stack we build on
Open-source models
Frontier APIs
Retrieval and vector
Serving and ops
Eval and safety
"We had spent fourteen months and seven figures trying to build a sovereign knowledge engine with a Big Four partner. MindMap had a working pilot against our actual policy corpus in nine weeks, in our data centre, with a regression suite of eleven hundred questions that we still run nightly."— Group CTO, Pan-Regional Insurance Holding
How we work together
Common questions
Do you work with open-source models or only frontier APIs?+
Both, and the choice is driven by your constraints, not our preference. For air-gapped or sovereign deployments we ship Llama 3, Mistral, Qwen, or Phi on your hardware. For cloud-tolerant workloads where latency or capability is the priority we use GPT-4o, Claude 3.5, or Gemini under your enterprise contract. We routinely run hybrid stacks where sensitive inference stays on-prem and non-sensitive draft generation calls a frontier API behind a redaction proxy.
How do you prevent hallucinations in a regulated context?+
Three reinforcing layers. First, retrieval grounds every answer in source paragraphs that are returned with the response as inline citations — if the corpus does not support an answer the system says so. Second, an answerability classifier blocks generation on out-of-scope questions. Third, an automated faithfulness eval scores every production response and feeds a daily drift dashboard. The combination drives hallucination rates below one percent on our regulated banking deployments.
What does sovereign deployment actually involve?+
Your data, weights, and prompts never leave your network. We deploy quantised open-source models on your GPUs — typically a small cluster of L40S or H100s sized to your throughput — served through vLLM behind your existing identity provider. There is no outbound internet, no telemetry to a vendor, no model-improvement clause in the licence. We have completed this pattern inside central banks, defence agencies, and a national health system.
How long does a typical engagement take?+
A discovery sprint is two weeks. A first production deployment is six to nine weeks from the end of discovery if the data is reasonably clean, longer if we are also doing data engineering. The accelerator library shortens what would be a six-to-nine month ground-up build by roughly seventy percent.
How do you handle model evaluation after go-live?+
Every deployment ships with an evaluation harness wired into CI that runs on every change. In production we sample a fixed percentage of live traffic, score it against LLM-as-judge and rule-based checks, and surface drift on a dashboard you and we both watch. A drop below your agreed threshold triggers an alert and an automatic rollback to the last green checkpoint.
Who owns the IP — the prompts, fine-tunes, and code?+
You do. Our standard contract assigns all custom prompts, fine-tuned weights, evaluation data, and bespoke code to the client on payment. The MindMap accelerators we bring as starting points remain licensed to you for the life of the deployment with no metered usage.
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