Capabilities

Three engineering practices, one architecture standard.

What we engineer, and what each practice produces in your hands. How you engage us to do it is set out under services.

Retrieval you can check

Typical deliverable: an internal answer layer over your document estate, with citations back to the source page and access that respects existing permissions.

An answer layer over the documents, threads and decisions your team already has — where every answer arrives with the source it came from, and access follows the permissions you already enforce. When the embedding misses, retrieval degrades into browsing rather than a dead end, because the human-legible structure is kept alongside the vectors.

Data pipelines

Chunking, parsing, and cleaning structured and unstructured data — PDFs, media, APIs — for long-context model ingestion.

Hybrid and multimodal search

Vector search (PostgreSQL/pgvector, FAISS) layered over human-legible structure — including multimodal indexes that fuse what is visible, audible, and said, at scene granularity.

In production: corpus — one search over five knowledge stores · sainma — ask a video library a question, get the clip

Agents your governance team will approve

Typical deliverable: an agent system your governance team can approve — human gates where money or reputation moves, a complete audit trail, and a cost profile you can predict.

AI does the hours; people keep the judgment. That means a human gate before money moves, a run you can watch in real time rather than a report you read afterwards, and model calls reserved for the places judgment is actually required — the split that cut one of our own pipelines from about $420 a sweep to about $105.

Orchestration with controls

Approval gates before spend, live observability of every agent's work, separated powers so no single model can fabricate its way into the output.

Model spend

Models where judgment is genuinely required, deterministic code everywhere else. The split is the discipline; the audit trail is the proof it held.

In production: swarm — auditable multi-agent research · nearby — territory to booked meeting, 2¢ a business · frontline — 24/7 customer-facing agents

Safety invariants in code

Typical deliverable: the same system, running entirely inside your VPC, with the evidence your security review will ask for — and the things it must never do enforced in code, not prompts.

“The AI must never do X” is an engineering requirement, not a prompt-writing exercise. We make the forbidden state unrepresentable — in the schema, the field names and the API responses — so no code path can produce it. A prompt is a request. A filter is a guarantee.

Runs where your data already lives

Inside your VPC on Amazon Bedrock or Google Vertex AI, with zero-data-retention configurations and PII masking in the middleware. Nothing routes through infrastructure we own.

Uncertainty becomes a question

Deterministic filters as the guarantee behind the prompt, and confidence gates that turn a low-confidence answer into a question for a human instead of a guess in production.

In production: Meai — a safety-critical platform that is structurally incapable of calling food “safe” · AutoTest — self-healing that may repair the path, never the assertion

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