Our story
Named for a standard.
Built to meet it.
Gnosis is the old word for knowing a thing yourself, from the source, instead of being told it. It is the only knowledge a buyer can bank on, and the only kind an AI should be trusted with. We named the company for the standard and then built the systems to meet it: five years of AI research and technical operations, then seven months building the production portfolio that runs today, agents that carry their history forward under rules a database enforces. Every system in this story is founder-accountable and human-agent built. Cap Dawes owns delivery and every consequential commitment; the persistent agents beside him investigate, design, build, and challenge. We don’t prototype the pattern on your time. We already run it.
Our evolution
The persistence problem
AI research and technical operations, before the agent era had a name. The wall was the one every builder still hits: an assistant that is brilliant for an hour and a stranger the next morning. Recall is not memory. A briefing is not a life. Everything since has been an answer to that.
Personas, memory, and a canon
The archive our fleet still runs on begins in January 2023. Named assistants with memory files and reasoning frameworks. A co-authored, novel-scale canon that needed a state kernel under the model to stay consistent. Real-time data models over dozens of live feeds. The first autonomous workflow agents. The pattern under each was the same: durable state, multi-source ingestion, deterministic decisions.
The accelerant
The portfolio that runs today, all of it February onward. The first production agent platform: multi-channel, memory-backed, tool-using, a fleet of specialized agents under one gateway. Then the cognitive runtime, the compliance marketplace, and the governed fleet below. Five years of research became seven months of shipping.
A production cognitive runtime
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: a compliance marketplace that passes audit (AI extraction → deterministic rule engine → human review → e-signature, with a placement supply chain behind the gate), a production front desk answering a public phone number, real-time consumer platforms, client-facing dashboards, and a stateful AI engine. Hardened by default: rate limiting, AV scanning, idempotent webhooks, audit trails.
The name, said out loud
Gnosis is the old word for knowing a thing yourself instead of being told it. We had been building to that standard for months before we wrote down what it added up to: identity-bearing AI, agents that carry history, judgment, and commitments forward while code bounds what their actions can do. The mind is trusted. The hands are governed. Every consequence leaves a receipt.
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, and the watch that keeps them correct after launch. 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 9,616 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: it does not reset between sessions or change without a trace.
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, $8.35Msaved on a single renewal, and the policy that put20,000+ 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 proof page.
Since 2021, the research. Since February 2026, the fleet.
I have been doing AI research and technical operations since 2021, and shipping the production agent portfolio since February 2026. 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 answers our phone, keeps our books, 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, and the agents that work beside me will have read the record before you arrive. Why that changes the work, in one essay: The Agent That Gives a Damn.
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.