AI platforms development
AI that knows your business
Enterprise-grade AI platforms — from RAG systems to AI-native apps built from scratch — that are robust, extensible, and integrated into the systems you already run.
You had a problem. Now you don't.
Before
A general model knows everything except the thing you need it to know: your documents, your data model, your rules, your customers. Ask it about those and it invents an answer that reads perfectly.
After
We build the retrieval, grounding, and evaluation layer that makes a model answer from your material and cite where it came from — then wrap it in a platform your team can extend without calling us.
What we build
Four layers, each of which can be the whole engagement.
Retrieval-augmented generation
Your documents, tickets, and records made answerable — chunked, embedded, ranked, and cited, so an answer can be checked rather than trusted.
- Hybrid search
- Re-ranking
- Citation and provenance
- Freshness and re-indexing
AI-native applications
Products where the model is the product, not a feature bolted to the side. Built from scratch, designed around what a model is actually good at.
Data and ingestion pipelines
The unglamorous half that decides whether any of it works: connectors, parsing, deduplication, permissions carried through from the source.
Evaluation and observability
Answer quality scored against a golden set, plus tracing and cost attribution per request, so quality and spend are both facts rather than feelings.
How it works
- 01
Find the questions
We collect the real questions your people and customers ask, and the answers that would be correct. That set becomes the specification and the test suite at once.
- 02
Prove retrieval first
Before a word of generation, we measure whether the right source material can be found at all. Nothing downstream can beat this ceiling.
- 03
Ground the answers
Generation constrained to retrieved material, with citations, and an honest “I don't know” when the material is not there.
- 04
Integrate and permission
Wired into your auth and your systems, so a user can only ever get answers from material they were already allowed to read.
- 05
Harden and hand over
Load tested, cost modelled, documented, and handed to your team with the evaluation suite that proves a change is an improvement.
How an answer gets grounded
Retrieve
Hybrid search over your documents, ranked for relevance.
Augment
The best passages assembled into context, with their sources.
Generate
An answer constrained to that context, and cited.
Don't take our word for it
The application is excellent, outperforming the existing iOS version and demonstrating the high quality of the code they produced. The team is proactive, taking the initiative to work quickly, respond fast, and work effectively at all times.
What we build with
Models
Claude · GPT · Gemini · Open-weight models
Retrieval
pgvector · Elasticsearch · OpenSearch · Redis
Platform
Node · Python · Postgres · Kafka
Cloud
AWS · Azure · GCP · Kubernetes
Stuff you'd normally have to email us about
Retrieval first, almost always. It is cheaper, it updates the moment your documents do, and it can cite its sources. Fine-tuning earns its place for tone, format, and narrow classification — not for teaching a model facts that change.
Constrain generation to retrieved material, require citations, and measure groundedness on a golden set. An honest “not in the documents” is a correct answer, and we treat it as one.
No. We use enterprise endpoints with training explicitly disabled, and where that is not acceptable we deploy open-weight models inside your own infrastructure.
Ready to make your data answerable?
Tell us what your team keeps digging for. We'll tell you whether retrieval can find it, and what that takes.
Book a feasibility sprint