1 vendor-registry-eu.csv
Rows 12–14, status column approved, region scope EU-27.
Services · Generative AI
The demo always works. We build for the day after.
Five practices under one discipline — a cited answer, a connected system, a conversation, an in-product suggestion, a generated asset. Each one grounded in your own data, evaluated against your own cases before it ships, and left with a person in the loop wherever being wrong is expensive.
Model-agnostic — hosted or in your cloud
- GPT
- Claude
- Gemini
- Open-weight
The problem, before any of this
Unstructured material — documents, conversations, tickets, systems — that nothing downstream can use yet
How you check it
The answer arrives with its receipts
A generated sentence is worth exactly as much as the passage underneath it. So we build retrieval that keeps the two attached — every clause carries the source it came from, and you can open it.
“Which translation vendors are approved for our EU regulatory filings, and who signs off?”
Three vendors are approved for EU regulatory filings: Lionbridge, RWS and Semantix1 Each must hold current ISO 17100 certification, re-verified annually before renewal2 Filings for Germany and Austria additionally require sign-off from the regional compliance lead, not the filing owner3
2 translation-policy-v2.pdf
§3.1 — “certification shall be re-verified annually prior to contract renewal.”
3 compliance-notes.md
§4, DE/AT addendum — approval authority sits with the regional compliance lead.
3 of 3 clauses grounded — an answer with an unsourced clause is held, not shipped.
What's inside Generative AI
Five practices, one grounding discipline
Not a sequence — pick whichever maps to what you're building. Each one turns unstructured material into something a model, or a person, can actually rely on.
Before any of this: a stack of pages nobody has indexed yet. Every practice below starts the same place — turning unstructured material into something a model can use correctly — then diverges based on what you're actually shipping: a cited answer, a connected system, a conversation, an in-product suggestion, or a fully custom build.
Retrieval infrastructure
RAG Solutions
Retrieval-augmented generation is only as good as the pipeline feeding it — ingestion, chunking, embedding and retrieval tuned to your documents, not a generic template. Every passage keeps its origin through the pipeline: an answer that cites its sources, not one that only sounds right, built to find the one paragraph that answers the question across repositories and languages.
- Production RAG pipelines tuned to your document set, not a generic template
- Answers traced back to source documents, not the model's general training data
- Real-time and batch pipelines behind retrieval — the plumbing most "RAG demos" skip
- Retrieval tuned and evaluated against your own query patterns, not a generic benchmark
A model that has only read the dictionary will define your terms. One grounded in your documents will quote them — which is the difference between an answer that sounds authoritative and one that is.
Protocol infrastructure
MCP Solutions
The Model Context Protocol gives your internal tools and data one contract instead of a bespoke integration per AI application. We build the MCP server once — Claude, Copilot Studio, ChatGPT or your own agent can all call it the same way, so a new app or a new model doesn't mean rebuilding the connector.
- MCP servers exposing your internal tools, APIs and data sources to your AI apps and agents
- One integration surface instead of a bespoke connector per AI app or per team
- Scoped, auditable tool access — a model can call what you expose, and nothing else
- Works alongside a RAG pipeline or standalone, wherever an agent needs to act, not just answer
Grounded conversation
Building AI Chatbots
A chatbot is easy to demo and hard to trust. We build conversational interfaces grounded in your actual knowledge base — with escalation paths to a human, logging you can audit, and the same guardrail discipline we apply to full agentic systems, scoped to a conversation instead of a multi-step workflow.
- Customer-facing and internal support chatbots grounded in your own data
- Escalation to a human at the point a conversation needs one, not after
- Full conversation logging and evaluation, so you can see what it's saying
- Deployed on your channels — Web, Slack, Teams, WhatsApp
Do we cover water damage from a burst pipe?
Yes — sudden and accidental water damage is covered under §12.3 (Water Damage). policy-homeowners-v4.pdf §12.3
Routing to a human — claim involves prior damage history. → Priya, Claims
Conversation logged · 1 source cited · 1 escalation
In-workflow assistance
AI Copilots
Not every AI feature should act on its own. A copilot suggests — inline, inside the tool your team already uses — and a person accepts, edits or ignores it. We build that pattern into your product or your internal tools: draft replies, code and query completion, suggested next steps, wired to just enough context to be useful and no standing authority to act without someone saying yes.
- Inline suggestions inside your product or your team's existing tools — not a separate app to switch to
- Code, query and draft-reply completion, scoped to the context a task actually needs
- A person stays the last step — suggestions are accepted, edited or dismissed, never auto-applied
- The same evaluation and logging discipline as every other practice here, so you can see what it's suggesting and why
A suggestion is not a decision. The interface stays the team's own — the model offers, a person disposes.
Purpose-built generation
Custom GenAI Applications
Sometimes the right answer isn't a chatbot or a plug-in — it's a purpose-built application around a specific job: generating and reviewing product imagery and video, converting speech to text and back, or wiring generation directly into a workflow your team already runs. We build these the way we build everything else here — with the evaluation harnesses, schemas and human review gates that keep a generative feature from becoming a liability, not a demo that happens to work once.
- Product and marketing image and video pipelines, with brand-consistent style controls
- Speech-to-text and text-to-speech, built into a product or workflow rather than bolted on
- Structured output — JSON, forms, reports — instead of free text your app has to parse
- Human review and moderation gates before anything generated ships externally
The style controls a batch is checked against — palette, tone, do-not-use — held constant across every variant, with a reviewer able to reject anything that drifts before it ships.
Let's scope your Generative AI project
Tell us what you're trying to ship — we'll tell you what's realistic in the first two weeks.