Responsible AI Integration
Deploy AI you can explain
We build retrieval-augmented assistants, chat workflows, and automation that keep data sovereign, auditable, and CPU-friendly. Every engagement ships with operating manuals so you can run it without us.
Retrieval that stays grounded
We map your source material, design chunking, and log citations so every AI answer points back to an approved document.
Operational transparency
Prompts, embeddings, and evaluator configs are stored as code. You know what changed, when it changed, and why.
Human-in-the-loop by default
Moderation queues, escalation workflows, and BitNet rubric scoring keep humans in charge of final decisions.
Start with verified knowledge
Before writing a single line of code, catalogue the truths your assistant will rely on. Tag each source with owners and review dates so the system never quotes expired guidance.
- Inventory policies, SOPs, and public documentation.
- Decide which collections can leave your network (most stay on-box).
- Define redaction rules for personal or contractual data.
Build a CPU-first retrieval pipeline
Low-latency RAG does not require GPUs. We containerise embeddings (e.g. `intfloat/e5-small`), vector stores, and BitNet inference so you can run on commodity hardware.
- Chunk documents with semantic transforms, not blind token limits.
- Store embedding + source metadata so citations are automatic.
- Keep prompts versioned (Git) and tie them to release notes.
# Example: launch a local retriever + BitNet evaluator
docker compose up retriever bitnet --build
# Ingest content with provenance tags
pnpm tsx scripts/ingest.ts --source ./knowledge --collection compliance --provenance "Policy Register v3"
# Verify latency stays within CPU budget
pnpm tsx scripts/probe.ts --questions ./checks/security.jsonMeasure understanding continuously
We wire BitNet or llama.cpp evaluators to grade free-form reflections using rubric prompts agreed with your subject matter experts. MCQ gating keeps the human benchmark honest.
- Track answer provenance, latency, and evaluator score in a single log entry.
- Review false positives/negatives weekly; adjust retrieval or instructions instead of masking symptoms.
- Push anonymised learnings into your internal RAG if, and only if, consent is active.
Resources to start today
We give before we take. Explore these official tools and playbooks freely.
Use the playbook to map context, govern access, and articulate risk treatments before launch.
CPU-friendly large language model from Microsoft. We package it in /test-server for local evaluation.
Step-by-step notebooks for chunking, indexing, and evaluating retrieval quality before wiring up a model.
Understand the regulator's expectations on explainability, DPIAs, and human oversight.