Custom AI
AI agent development
We build custom AI agents for teams that need production outcomes, not demos. Every engagement is fixed-scope, KPI-tied, and runs end-to-end with the founder. Agents ship with eval harnesses, observability, and human-in-the-loop gates baked in from day one.
What "AI agent development" means at QwertyBit
We build custom AI agents that take real work off human hands. Not chat widgets, not generic copilots — production systems that run inside your operations, tied to a KPI you already care about. A well-scoped agent replaces 30–80% of the manual toil in a single workflow and keeps doing it while your team focuses on higher-leverage work.
The agents we ship
- Document-processing agents — contract review, KYC intake, regulatory filings, invoice parsing. Our contract risk scoring engine cut manual review time by 88% for an insurance client.
- Internal-knowledge agents — Slack/Teams-embedded assistants trained on your wiki, ticketing, and codebase. See the internal knowledge base agent case.
- Sales and CRM agents — lead qualification, behaviour scoring, follow-up automation, pipeline hygiene. The sales pipeline automation engagement tripled lead-to-deal conversion.
- Compliance and audit agents — policy-aware summarisation, audit-trail generation, risk flagging.
- Multi-agent workflows — when a single prompt is not enough, we orchestrate teams of specialist agents (planner, executor, reviewer) via CrewAI or LangGraph.
Why clients hire QwertyBit over a generic dev shop
- Senior-only delivery. Every line is written by engineers with 5+ years of production experience. No juniors training on your data.
- Fixed scope, transparent pricing. The quote lists build cost, monthly run cost, and the cost of doing nothing. See our engagement approach.
- KPI-tied from day one. If the agent does not move the number it was scoped to move, it does not ship.
- You own everything. Code lives in your GitHub from commit one. Prompts, eval harnesses, infrastructure — all yours. If we part ways, your engineers inherit a working system, not a black box.
- Evals before production. Every agent has an evaluation harness that catches regressions before your users do.
The stack we build on
We default to Anthropic Claude for high-reasoning and long-context agents, CrewAI for multi-agent orchestration, and LLM Studio when on-prem deployment is a hard requirement. The right model and framework is chosen in the feasibility phase — not by default.
How to start
The first step is a discovery conversation with the founder. Bring one painful workflow and we will tell you within a week whether an agent is the right shape of solution, what it would cost, and what KPI it should move. Book a business audit or read our four-phase approach.
Built with
Frontier LLMs
Anthropic
QwertyBit builds production AI agents on Anthropic Claude for high-reasoning, long-context, and compliance-aware workflows where steerability matters.
Multi-agent orchestration
CrewAI
QwertyBit builds multi-agent systems with CrewAI for workflows that need specialist agents planning, executing, and reviewing in sequence — not a single oversized prompt.
Local & on-prem LLMs
LLM Studio
QwertyBit deploys on-premise LLMs via LLM Studio for clients with strict data-residency requirements — Llama, Qwen, Mistral, Gemma, DeepSeek, fully on your hardware.
Services FAQ
What business owners ask before signing
A chatbot responds to prompts. An AI agent perceives context, plans a course of action, takes steps across tools and data sources, and verifies its own output. QwertyBit agents can read and write to your systems (CRM, ticketing, docs), call external APIs, and escalate to humans on high-risk decisions. They are workflow actors, not conversation toys.
Ready to see where agents can take cost out of your business?
Tell us about the process you want to optimise. Vlad personally reviews every brief and replies within one business day.