Our story
Four years building AI
that survives production.
Gnosis Labs didn’t start with the AI wave. We have been building persistent-memory, stateful, multi-agent systems since before they had names: first as our own infrastructure, now as the substrate we deploy for the companies that need it. Every system in this story was built by one engineer and the fleet he runs; that leverage is the product. We don’t prototype the pattern on your time. We already run it.
Our evolution
The persistence problem
Our work began where most AI still breaks: memory. We built systems that retained identity and context across sessions, solving continuity and long-horizon state years before it became a product category. The discipline that defines us now started here.
Applied R&D across domains
A year of building deliberately across generative systems, quantitative real-time data models, and autonomous workflow agents. Each established an engineering pattern that anchors how we deliver today: durable state, multi-source ingestion, agent orchestration.
A self-hosted multi-agent platform
We stood up our own agent platform: multi-channel, memory-backed, tool-using, running a fleet of specialized agents under one gateway. The internal-agent-platform infrastructure most teams are only now attempting: we built and operated it.
A production cognitive runtime
We architected a standalone cognitive runtime: persistent semantic memory, scheduled reflection cycles, multi-channel presence, governance enforced in the database. Live in production since Q1 2026; later migrated between databases with twelve minutes of downtime and fail-closed security proven throughout.
From one agent to a governed fleet
A seven-agent operational fleet over a live data warehouse, an autonomous portfolio manager that holds state across the whole estate, and a semantic-memory substrate the whole fleet queries mid-task. Multi-agent systems with persistent identity, database-enforced governance, and full audit. The real thing, not one prompt calling another.
Shipping to real users
Production systems serving live users and revenue: real-time consumer platforms, a compliance marketplace that passes audit (AI extraction → deterministic rule engine → human review → e-signature, with a placement supply chain behind the gate), client-facing dashboards, and a stateful AI engine. Hardened by default: rate limiting, AV scanning, idempotent webhooks, audit trails.
The pattern, made repeatable
Everything we learned building our own fleet is now the substrate we deploy for clients: control planes, memory layers, document pipelines, and orchestration. The systems aren’t slideware; they’re running. Two of them lead the work page now, screenshots and receipts attached.
What we build
Agent control planes & orchestration
Governed multi-agent platforms: scoped tool access, database-enforced rules, audit trails, human-override paths, and autonomous multi-phase execution that completes real work.
proof: our seven-agent operational fleet + an autonomous orchestration harnessPersistent memory layers
The memory most “AI agents” are missing: semantic + lexical retrieval, auto-ingest, MCP-native. Verifiable, not claimed.
proof: a semantic memory engine over 6,160 working sessions, run in production dailyDocument & data-ingestion pipelines
Ingest from many sources, normalize messy multi-source data, decide deterministically: AI for perception, code for verdicts, humans for the edge cases. Built to pass compliance review.
proof: a vendor-compliance pipeline in production + a 40+ API real-time data engineDurable, stateful AI systems
Event-sourced applications where state, memory, and consequence persist across long sessions. The hard kind of AI that doesn’t forget and doesn’t drift.
proof: a fully-tested, containerized stateful AI engine + a live cognitive runtimeHow we work
Verify before claiming.
A surfaced failure beats a silent defect.
Ship dark, flip flags in production.
Every phase gates on a failing test written first.
The founder
I am Cap Dawes. I run Gnosis Labs. Before I built production AI systems, I spent thirteen years on the other side of the table: five buying and grading technology for the Air Force, eight selling and staffing it in private industry. This is the part of the record that matters to you.
Five years grading vendors
Air Force Academy, systems engineering management. Then active duty as an acquisition program manager, 2013 to 2018. My job was deciding whether vendor promises would survive delivery: $879M in contracts managed, 47 contractor performance reviews graded, $8.35M saved on a single renewal, and the policy that put 20,000+ tablets on the Air Force network. I have read more vendor proposals than most vendors have written. I know which promises fail in production, because grading that failure was my job. Every receipt on this site exists because I was the buyer once, and bare claims never survived my desk either.
Eight years in IT services
After the Air Force I went into IT services: staffing, consulting, and workforce delivery for clients from emerging growth companies to the Fortune 500. I carried a quota and managed accounts, and I watched operators live with software that failed quietly, vendors that disappeared after the invoice, systems built by people who never sat in the seat. So I built the systems myself. Intake, compliance, back-office operations: production AI running a real business, not a pilot. They are still running. You can see them on the work page.
Since 2021, the fleet
I have shipped production agentic systems since 2021. The fleet that runs Gnosis Labs is the same class of system I build for clients: persistent memory, database-enforced governance, real uptime. It is not a demo. It captured the lead that brought you here, and its numbers are on this site with methods and limits attached. Click any of them. Every engagement is founder-led. When you book a session, you get me.
If you’re past the chatbot phase, this is the team.
We design the control plane, ship your first production agents, and leave your team the playbook. Fixed scope, fixed price. If it isn’t working, you don’t pay the back half.