Custom AI Applications
Production-grade AI applications built end-to-end — from data layer to UI — on your stack.
We build the application your business needs from the ground up. RAG, extraction, classification, semantic search, recommendation. Yours to own, deploy, and extend.
What we ship
Concrete deliverables, not features.
01
Production-ready RAG with eval suite
Domain-specific retrieval-augmented system with hybrid search, eval harness, observability, and cost controls. Not a chatbot demo — a system your operations team relies on daily.
02
Extraction & classification pipeline
From unstructured input (contracts, calls, claims, support tickets) to structured business data. Reviewed against ground truth, measured continuously, integrated with your downstream systems.
03
Semantic search with domain tuning
Search that returns what users meant, not what they typed. Tuned to your vocabulary, indexed on your data, deployed in your application surface.
04
Full-stack web application
The AI capability shipped inside an application your team or your customers actually use — with auth, audit logs, role-based access, and a UI that respects the user.
How it works
Discovery first. Weekly cycles. Pause anytime.
- Discover01
Discovery sprint
Two weeks, fixed scope. Working prototype on your data, architecture decision record, scoped follow-on proposal.
- Design02
Build
Weekly cycles with demos and decision points. Scope can be adjusted, paused, or ended after any cycle. No surprises at the end.
- Build03
Ship to production
Code in your repositories. Deployment in your cloud by default. Runbook written for your team to operate, monitor, and extend it.
- Operate04
Optional retainer
Monthly retainer to maintain, monitor, and extend the system. Quarterly review against business outcomes. No auto-renew.
Where it compounds
Scenarios where this work pays back.
01 · Use case
Knowledge bottleneck across 10k+ documents
Your support team, sales engineers, or domain experts spend hours searching internal docs. RAG over your knowledge base surfaces answers in seconds, with citations.
Typical compounding: support handle time down 15–25%, sales engineer pre-meeting prep cut from hours to minutes.
02 · Use case
Manual extraction at volume
Your operations team extracts structured fields from 1,000+ documents/week — contracts, invoices, claims, forms. AI extraction with human review at the right thresholds.
Typical setup: 90%+ auto-extraction rate, human review on the 10% threshold. Throughput up 3–5×.
03 · Use case
Customer-facing intelligence in your product
Your SaaS product needs an AI feature that actually works — search, recommendations, smart defaults, agents. Built into your stack, measured on customer outcomes.
Typical: feature shipped to 100% of customers with eval-driven rollout, not behind a beta flag indefinitely.
Engagement
Fixed scope after discovery.
- Duration
- 2–5 months
- What you get
- Production-deployed application, code in your repos, runbook for your team
- Start with
- Discovery sprint, 2 weeks
Frequently asked
Questions before the first call.
Do we own the code and the models?
You own everything. Code lives in your repositories from day one. Fine-tuned model weights are yours. We don't train on your data for any other client and we don't sell anything you produce back to anyone.
Can you build on top of OpenAI / Anthropic / open-source models?
Yes — we're model-agnostic. We benchmark on your data during discovery and recommend a stack. Production systems often use multiple providers (routing by cost, capability, latency) with abstraction so you can switch without re-architecting.
What does 'production-ready' mean in practice?
An eval suite that runs in CI on every change. Observability with logged inputs, outputs, costs, and latency. Cost controls and rate limits. Auth and audit logs. A runbook covering deployment, rollback, incident response, and model upgrades. Not all five — all of them.
How do you measure success?
Defined during discovery, written into the contract. Examples: support handle time, extraction accuracy against ground truth, time-to-first-response, weekly active usage. Measurable, not 'better experience'.