Engineers who build AI that ships
Cognilayer was founded by a team of engineers with experience from Fortune 500 companies like Amazon, LexisNexis, and Shutterfly, building large-scale data and AI systems in demanding production environments. We believe AI and software should solve real problems, not add complexity, and we work alongside our clients from the first idea to production deployment.
What we do
We design, build, and deploy custom AI, data, and automation systems for businesses. That spans conversational and voice agents that handle real customer and operational workflows, MCP infrastructure that safely exposes internal tools and data to LLM clients, retrieval systems grounded in private knowledge, and the data platforms and pipelines underneath it all.
Our work runs the full stack: multi-agent backends, cloud architecture on AWS, native mobile apps, and the integrations that connect them to the systems a business already relies on. We treat security, governance, and reliability as first-class requirements at every layer.
How we work
We are a small, senior team. The people who scope your project are the people who build it, with no hand-offs to a junior bench. We move from idea to working software quickly, then harden it: real integrations, error handling, tests, and observability before anything reaches production.
We collaborate closely and stay accountable to outcomes. You get direct access to the engineers doing the work, honest tradeoffs instead of hype, and systems you can operate and extend after we hand them over.
How we think about AI
Governed by default
Scoped permissions, audit trails, and human-in-the-loop checkpoints are built in, not bolted on. AI that touches production data should be accountable for every action it takes.
Production, not demos
We ship systems that run against real workloads: live integrations, error recovery, observability, and test suites. A prototype that impresses is not the same as software you can trust.
Agents that do real work
Multi-agent orchestration, tool use, and retrieval grounded in your own data, designed to complete tasks end to end, not just answer questions.
Simple over clever
The smallest architecture that solves the problem. We reuse proven patterns, keep interfaces clean, and avoid complexity that future engineers have to pay for.