When ChatGPT can't read your documents. When your support bot needs to know your product. When your internal tool needs to reason across systems. We build the layer on top of LLMs that makes them actually useful: production-grade, owned by you, integrated with what you already run.
ChatGPT can write a poem about your industry. It can't tell you which of your 12,000 SKUs to recommend to a customer who just emailed asking for a replacement. It doesn't know your pricing rules, your support history, your contract terms, your domain quirks.
Custom AI is the work of giving an LLM the context, the data, and the guardrails to do something specific: well, repeatably, and at scale. That's the work we do.
Six things we build. Hundreds of variations.
Customer-facing bots that actually know your product, pricing, and policies. Internal copilots that pull from your wikis, code, or knowledge base.
Retrieval-augmented generation over your documents, support tickets, contracts, internal wikis, or any corpus. Searchable, citable, governable.
AI baked into your existing app: summarization, classification, generation, recommendation. Built to scale with your traffic, not break under it.
Multi-step AI agents that take actions across systems: read an email, check a database, draft a response, send it, log the result. With humans in the loop where they belong.
Transcription, summarization, scoring, and coaching insights from sales calls, support calls, or operations audio.
OCR + LLM pipelines that pull structured data out of contracts, forms, statements, and scans, including bad scans, handwritten notes, and edge cases generic OCR misses.
We're specific about what we don't do, because clarity on that is how you know we're honest about what we do.
Training a base model from scratch takes a hundred-million-dollar GPU cluster and a research team. That's not us, and it's not what 99% of businesses need. We use the best foundation models on the market and add the layer that makes them yours.
You won't sign a five-year contract for our SaaS. Everything we build runs on standard infra, owns standard models, and is yours to take to another vendor if we ever stop earning the work.
If a use case has a 60% success rate with current models, we'll tell you that, and recommend waiting, narrowing the scope, or solving it without AI.
The model is the smallest part. The retrieval layer is what knows your data. The orchestration layer is where the business logic lives. The evaluation layer is what makes it safe to ship. We build all four because skipping any one of them is how AI projects break in production.
87% reduction in document processing time. 14-person queue replaced. 3-week build.
Andrew F., Head of Product, Logistics SaaS company · Live for 9 months
Four phases. Working software in 8 weeks.
We sit with you, understand the use case, look at the data, and decide whether the project is worth doing. If it's not, we tell you. If it is, you get a phased proposal with a budget cap on each phase.
A working version on your data, evaluated on real cases. We demo it every week. Most projects get cancelled or expanded based on what we find here, and that's a feature, not a bug.
Hardening, integrations, evaluation harness, deployment, monitoring. The boring 70% of the project that 90% of AI agencies skip.
Your team can run, monitor, and extend the system. Documentation, training, runbooks. We're available after, but you don't need us.
Most production builds run $15,000-$75,000 depending on scope, integrations, and data complexity. Scoping phase is free. Build phase runs against a budget cap, billed weekly against actual work, with phase-gate stop points where you can pause, continue, or end.
Scoping phase is free · No commitment required
Tell us the problem. We'll tell you whether AI solves it, and if it does, what that actually costs and looks like.