We build the AI that does the work
Voice agents that answer the phone, retrieval that cites its sources, and automations that run the steps your team keeps doing by hand — engineered to survive contact with production.
AI that ships, then keeps working
We do two things: build AI systems, and build the products they run in. Narrow on purpose — it is the only way to get either one right.
AI engineering
Agents, retrieval, and voice systems grounded in your data and wired into your tools — not a thin wrapper around a public model.
Web development
The applications and sites the AI actually lives in. Fast, accessible, and built so the model is a feature rather than a bolt-on.
One workflow first
We start with a single process that costs you real hours, put it in production, and measure it before widening the scope.
Owned after launch
Models drift and APIs change. We monitor accuracy, cost, and failure rates, and keep the system correct as both move underneath it.
Six things, done properly
Every engagement is AI work or the product surface it ships on. If a project needs something outside this list, we will tell you and point you somewhere better.
Workflow automation
Agentic pipelines that run the multi-step processes your team repeats by hand, across the tools you already pay for.
- Multi-system orchestration
- Human approval checkpoints
- Failure alerting and retries
Voice AI agents
Phone agents that answer, qualify, and book — inbound or outbound — and hand off to a human the moment they should.
- Inbound call handling
- Outbound qualification
- CRM and calendar hand-off
RAG & knowledge systems
Retrieval over your private documents that answers from the source and cites it, instead of inventing a confident guess.
- Ingestion and chunking
- Evaluation harness
- Citations and traceability
Chatbots & assistants
Assistants on your site, product, WhatsApp, or Slack that resolve the question rather than deflecting it to a form.
- Site and in-product chat
- WhatsApp and Slack
- Escalation to a human
MCP servers & integrations
Your internal systems exposed as typed, permissioned tools that Claude and other agents can call directly.
- Custom MCP servers
- Auth and scoping
- Existing API wrapping
Web development
The applications and marketing sites the AI ships inside — built to be fast, accessible, and genuinely maintainable.
- Full-stack applications
- Streaming AI interfaces
- Marketing sites
What we build on
The models, frameworks, and infrastructure behind the systems we ship.
Frequently asked questions
Answers to what teams usually ask before we start. Anything missing, ask us directly.
What kind of AI work do you take on?
Workflow automation, voice agents, retrieval systems over private data, chat assistants, and MCP servers — plus the web applications those ship inside. We deliberately do not take DevOps, cloud migration, or analytics engagements any more. If that is what you need, we will say so early rather than stretch to fit.
How do you stop a RAG system from making things up?
Retrieval quality is an engineering problem, not a prompt problem. We build an evaluation set from your real questions before writing the pipeline, then measure retrieval and answer accuracy against it on every change. Answers cite the source passage, and the system is built to say it does not know rather than fill the gap. You get the eval harness, so the number stays visible after we hand over.
What does it actually take to launch a voice agent?
Usually four to six weeks for a first production line. Most of that is not the model — it is mapping the call flows, deciding what the agent must never do, wiring the CRM and calendar hand-offs, and tuning latency until the conversation stops feeling like a robot. We start with one call type, run it alongside your team, and widen only once it holds up.
What is an MCP server and why would we want one?
Model Context Protocol is a standard way to expose your internal systems to AI agents as typed, permissioned tools. Instead of rebuilding a bespoke integration for every assistant you adopt, you build the server once and any MCP-capable client can use it. It is becoming the default integration layer, and getting there early means the next model you adopt is a configuration change rather than a rewrite.
Where does our data go, and which models do you use?
We choose the model per workload and tell you which one and why. Where policy or sensitivity demands it, we run open-weight models on infrastructure you control instead. Your data is not used for training, we scope retrieval to what each agent legitimately needs, and every tool call an agent can make is enumerated and permissioned rather than open-ended.
Who owns the code and the systems you build?
You do, entirely. Everything ships into your repositories and your accounts, with the prompts, evaluation sets, and infrastructure definitions included. There is no proprietary runtime you have to keep paying us for, and no lock-in that makes leaving expensive. We would rather be kept because the work is good.
What happens after it goes live?
This is the question worth asking any AI agency. Models get deprecated, providers change behaviour, and the accuracy you launched with drifts. We instrument accuracy, cost per run, and failure rate from day one, alert on regressions, and offer an ongoing engagement to keep the system correct. A system nobody is watching quietly stops working long before anyone reports it.
Contact DotLair
Tell us what you're trying to automate, answer, or ship. We'll come back with a straight answer on scope, timeline, and whether it's something we should be building for you at all.