What's included
- Chatbots and assistants built on LLMs
- Process automation: triaging email, reading documents, drafting content
- Integrating existing models (OpenAI, Claude, and others) into your systems
- Internal AI tools for your team
- Search and retrieval over your own knowledge base (RAG)
The opportunity
The cost of hiring has not made manual work cheaper — it is the same busywork, delivered the same way, every single day. That is work for software that does not need to sleep.
LLM systems are powerful and, on their own, unreliable enough to be dangerous. They earn trust only inside workflows that run on rails: grounded retrieval, evaluation, and a human where a decision actually matters.
Our approach
We start from the process, not the model. What is the repeated manual work, where does judgment matter, and what would “good enough to trust” look like?
We build grounded pipelines: retrieval over your own data, clear prompts, structured output, and evaluation harnesses that tell us when a change is an improvement and when it is a regression.
We keep the human in the loop where it counts, and we watch cost — a clever system that burns budget is a toy.
Business outcomes
Manual work routed to software, with a measurable slice of time handed back to the people who were doing it.
Faster, more consistent decisions grounded in your data, with every step open to audit.
Common questions
What can AI actually do for my business?
The honest answer is narrower than the marketing: it is very good at repetitive work with language in it — sorting incoming email, reading documents and pulling out what matters, drafting, answering questions from your own material, first-line support. It is not good at being trusted alone with a decision that costs money. We start by looking for the first kind.
Does our data end up training someone else's model?
Not with the business API tiers we build on — the major providers do not train on that traffic, and it is contractual rather than a promise. Where the material is sensitive enough that the question matters more than the answer, we can keep the whole thing on infrastructure you control.
What happens when the model gets it wrong?
We assume it will, and design for it. That means grounding answers in your own documents rather than the model's memory, evaluation that catches a regression before it ships, and a person approving anything that is expensive to get wrong. A system nobody checks is a system nobody should trust.
What does it cost to run?
There is a per-use cost, and it is easy to build something clever that quietly burns budget. We size it before building, choose the cheapest model that does the job rather than the largest one available, and put a ceiling and monitoring on it so a surprise shows up as an alert and not as an invoice.
Do we need a lot of data to start?
Usually less than people expect. Most of the useful work runs on documents you already have — procedures, past correspondence, a product catalog. You need a real process worth automating far more than you need a large dataset.
Worth reading first
- Past the chatbot: where language models actually save workNearly every site has a chat bubble in the corner. The work language models genuinely save is elsewhere — in internal tools no customer ever sees.
- WhatsApp as a sales channel: setup, wiring, and pitfallsThe lowest-friction channel a business has, and the easiest one to get your number blocked on. Most of what fails there is decided in the first week.
The tools
We work inside what you already have
Some of what we build with and connect to. The list grows with whatever our clients already run on, so anything missing here is still worth asking about.
Next.js
React
TypeScript
Tailwind
Node.js
Python
Flutter
GraphQL
PostgreSQL
MongoDB
Supabase
Firebase
Docker
Git
Vercel
Cloudflare
Anthropic
Hugging Face
LangChain
n8n
Make
Zapier
HubSpot
Zoho
Airtable
Notion
Stripe
Shopify
WordPress
WhatsApp
Telegram
Gmail
Sheets
Analytics
Figma
Ready to scope this engagement?
Tell us the shape of the problem and you get back scope, risks, and a recommended engagement.