AI products that survive the jump from demo to production.
We architect full-stack AI platforms — RAG, agents, evals, and the boring infrastructure underneath — so the product still works at week fifty, not just week one.
Why most AI platforms die at week twenty.
The demo works. The pilot impresses. Then real users arrive and the RAG hallucinates, the agent forgets context, latency triples, and the dashboard nobody built starts mattering. Building the demo is the easy part. We build for the part that breaks — eval frameworks, retrieval testing, observability, and the boring auth and access-control work that decides whether you actually get to scale.
Returned citation [3] for “30-day refund” but the source mentions no refund policy.
Agent forgot “second party” reference after turn 12 of the conversation.
Pass rate dropped 29pp on contract-amend.eval after the model upgrade.
Four shapes, one engineering team.
RAG knowledge platforms
Indexed, citation-backed knowledge platforms grounded in your data. Hybrid retrieval, re-ranking, and adversarial evals before every deploy.
See case studyAgentic workflows
Tool-using agents with human-in-the-loop gates for research, ops, and back-office. Plan, act, observe, route to a human when it matters.
AI copilots inside SaaS
Embed AI into an existing product without breaking the existing UX. Inline drafts, summaries, queries — with the right guardrails.
Vertical AI products
Domain-specific AI for legal, healthcare, fintech, and recruiting. Compliance-aware, schema-mapped, production-grade.
Side-by-side with a typical agency.
We don’t outbid the cheap shops, and we don’t pretend to be McKinsey. Here’s where the real practical difference lives.
The exact tools and why we chose them.
No mystery stack, no platform lock-in. You see what we use, you read why, and you own the keys on day one.
We pick per task and hot-swap. Claude for instruction-following, OpenAI for speed.
One database for vectors, BM25, and your domain — no separate Pinecone bill.
Stateful agents with checkpoints and human-in-the-loop primitives.
Adversarial regression tests on real samples before every deploy.
Streaming, RSC, edge-deployable — the product’s interface, not just the chat box.
You pick your cloud. We don’t bind you to ours.
Discover · Architect · Build · Ship.
Four stages, named timelines, named deliverables. No open-ended discovery. No moving goalposts.
Discover
We map your domain, data sources, and the failure modes that actually matter to your users.
- Domain + data map
- Failure-mode inventory
- Eval scenarios
Architect
We design retrieval, evals, guardrails, and the surface area users actually touch.
- Architecture diagram
- Eval suite
- Guardrails policy
Build
We ship the platform in tested slices, evaluating against the eval suite at every cut.
- Working platform
- Eval pass rate >95%
- Observability dashboards
Ship
Production cutover with hyper-care. We’re on-call for the first 72 hours, on retainer thereafter.
- Production cutover
- Runbook + SLAs
- Monthly eval report
Real numbers, named client.
A legal-tech founder needed contract analysis to be both fast and trustworthy. Off-the-shelf chatbots hallucinated; manual review took hours.
We built a hybrid Postgres + pgvector retrieval system on Next.js with citation enforcement, eval-driven prompt tuning, and a contract-aware UI.
40% faster contract creation. Citation-grounded answers users can verify. Built in 6 weeks, in production today.
Fixed price. Fixed scope. Public ranges.
We don’t hide pricing behind a sales call. Pick the tier that matches your stage. The discovery call confirms scope, not budget.
- RAG MVP on one knowledge source
- Citation enforcement + custom retrieval
- Eval framework with regression tests
- Production deployment on your cloud
- 3 months of post-launch support
- Fully customized to your domain
- Senior engineer owns the engagement
- Multi-source retrieval, agents, integrations
- Ongoing optimization and on-call
The questions we actually hear on calls.
How long does an AI platform take to build?
A focused RAG MVP ships in 4 weeks. A full multi-source production platform with integrations ships in 6–10 weeks. We don’t take 12-week projects without naming them as such.
How do you keep the AI from hallucinating?
Three things: retrieval-first prompting with citation enforcement, an eval suite that runs adversarial scenarios before every deploy, and a fallback policy when confidence drops. We test it the way users will break it.
What if we already have a database, CRM, or data warehouse?
Better. We connect to your existing data through APIs or direct queries. We’ve integrated with Postgres, Snowflake, BigQuery, Salesforce, HubSpot, and bespoke internal APIs.
Can we host this ourselves?
Yes. We default to your cloud (AWS, GCP, Azure, Vercel) and your API keys. No lock-in. You own the code from day one.
Does this need an ML team?
No. We don’t train models — we use frontier LLMs from Claude and OpenAI. The expertise is in retrieval, evals, prompts, fallbacks, observability — engineering work, not data science.
What does the monthly retainer cover?
24/7 on-call for production incidents, monthly eval reports with regression tests on real samples, and continuous improvement based on usage. It’s how the platform keeps getting better — not a maintenance fee.
Most AI projects fail not at the demo, but at the third sprint after launch — when real users find the edges. We build for that sprint. Eval-first, retrieval-grounded, fallback-aware, in production.