Built with
Every project includes
- Weekly demos in staging
- Documented rollback plan
- Docs and handover
- Support after launch
How we approach it
Most of the work is not the model. It is the retrieval layer that finds the right paragraph, the permissions that stop one customer seeing another's data, and the tests that tell you whether this week's prompt change made answers better or worse.
We pick the model per job, not per vendor. OpenAI, Anthropic's Claude and open models each have a place, and we often route between them on cost and speed so you are never locked to one provider.
See it working before you talk to anyone.
What we build
Generative AI & LLM Apps, end to end.
Internal copilots
One place for your team to ask questions across policies, wikis, tickets and shared drives, with links to the source.
Document intelligence
Contracts, invoices, forms and reports read, classified and turned into structured data your systems can use.
Search over your knowledge
Retrieval pipelines tuned for your content: chunking, embeddings and ranking tested against real questions.
Content and drafting tools
Drafts for proposals, replies and reports in your voice, with a person approving before anything goes out.
Customer-facing assistants
Assistants on your site or app that answer accurately from approved content and hand off cleanly to your team.
Model routing and cost control
The right model for each request, with caching and limits so usage costs stay predictable as you grow.
How it runs
From first call to production.
- 1
Discovery call
The problem, the people affected and what success looks like, in your words.
- 2
Written plan
Scope, success metrics and what you will see each week, agreed before any code.
- 3
Weekly builds
Working software in staging every week, not a reveal at the end.
- 4
Staged launch
Released in steps, with monitoring and a documented way back.
- 5
Handover or support
Docs, access and training, or we stay on to run and improve it.
Case studies
Work we've shipped
Enterprise Technology & Business Operations
Turning scattered business documents into answers employees can simply ask for
Read the case study →Academic Research / EdTech SaaS
Cutting literature review and qualitative coding time for academic researchers with an AI-assisted analysis suite
- 30
- PDFs processed per review batch
Dental / Healthcare SaaS
Turning lapsed dental patients back into recurring revenue with AI-personalized outreach
- 496
- Patients recovered across featured practices
- $33.5K
- Revenue recovered by one practice
Where it fits
Industries we build this for
Legal
Client intake, document review and matter updates, with confidentiality built in.
Accounting & Tax
Document collection, reconciliation and client chasing, automated.
Education & EdTech
Learning platforms, research tools and LMS at scale.
SaaS & Platforms
AI workflow platforms and the infrastructure under them.
No. We use API terms where providers do not train on your data, keep your documents in your own storage, and can run open models on your infrastructure when data must stay in-house.
Answers are grounded in retrieved sources, low-confidence questions are handed to a person, and every release runs against a test set built from your real questions.
Whichever fits the job on accuracy, speed, cost and data rules. We test candidates on your own examples before choosing, and design so you can switch later.
Yes. PDFs, scanned forms, spreadsheets, web pages and images are all common inputs. We test extraction quality on your actual files before committing to a design.
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