About Agilotek
Full-Stack Engineering, Built Around AI That Actually Ships
Agilotek exists because most teams don't need a chatbot bolted onto their homepage — they need an engineering partner who treats AI as one tool among many, not the whole pitch.
Our Story
Why Agilotek Exists
Most agencies pick a lane: an AI shop that wraps GPT in a chat widget, or a dev shop that treats AI as someone else's problem. We built Agilotek to close that gap — a single senior team that can train a custom model on Monday and ship the production web app that calls it by Friday.
We work with founders, operators, and engineering leaders who are past the stage of needing a demo and firmly in the stage of needing something that holds up under real traffic, real data, and real compliance requirements.
That means every engagement starts with the same question: is this actually an AI problem, a software problem, or both? We'd rather tell you the honest answer than the one that's easiest to sell.
At a Glance
- engineering disciplines under one team
- 8
- AI specialties: workflow automation and custom model training
- 2
- accountable team from data pipeline to production app
- 1
What We Build
AI Automation & Custom Model Training
Our AI work is split into two disciplines that usually ship together: automating the work people repeat, and training models for the work off-the-shelf models get wrong.
AI automation
- Document, email, and ticket triage with LLMs that route, extract, and draft — with a human approval step where it matters.
- Agents that call your real systems (CRM, ERP, helpdesk, internal APIs) through scoped, logged tool access.
- Retrieval pipelines over your own contracts, wikis, and records, with citations back to the source passage.
- Deterministic workflow code around every model call, so retries, timeouts, and fallbacks behave predictably.
Custom model training
- Dataset audits and labeling plans before any training run, because data quality caps model quality.
- Fine-tuning and distillation of smaller models when volume makes a large hosted model too slow or expensive.
- Held-out evaluation sets and regression suites, so every new model version is compared against the last one.
- Deployment with versioning, monitoring for drift, and a rollback path that doesn't need an engineer awake.
How We Operate
What We Actually Believe
We scope honestly
If AI isn't the right tool for your problem, we say so before we take the engagement — not after you've paid for it.
One senior team, every discipline
AI, web, mobile, and infrastructure work sits under one accountable team, not handed off between disconnected contractors.
Security and compliance from the start
Every system we design accounts for data handling, access control, and auditability from day one, not as a retrofit.
Built to run unattended
We measure success by what still works at 2am a year later, not by how well a demo goes on launch day.
Want to work with us?
Tell us what you're building and we'll tell you honestly how we can help.