About Recon
Why this exists
I'm an engineer. Six years at AWS, then ten months at a B2B SaaS AI company. That ten months is where Recon started.
Any customer question that needed a real answer kept routing back to engineering. Why is this account's usage off, why is this customer seeing an error, what plan are they on, which features have they enabled, why did their integration stop syncing. Not because anyone wanted it that way. Because engineering was the only team with access to the production database, the code, the error tracker, and the internal tools where the answer actually lived.
Support would file a ticket. A PM would ask in a thread. Sales would drop a question before a call. Each time, an engineer would drop what they were doing, spend 30 to 45 minutes digging across systems, paste the answer back, and try to remember what they were building before they got pulled in.
Multiply that across a growing customer base and engineering becomes a permanent triage layer for customer behavior. Support waits. PMs wait. Engineering context-switches all day. Customers get vague replies in the meantime. I left to build Recon so the next team would not have to route every customer question through engineering, and engineers would not have to keep being the bottleneck.
What Recon actually does
Recon is customer memory for Claude and ChatGPT. It connects once to your product systems, builds a customer-shaped memory (plan, usage, adoption, incidents, billing, tickets, admins, what changed, what stalled, what is at risk), and exposes it as an MCP server so your existing AI tools answer about your customers with real context.
It connects to the 13 tools your team already uses: your database (Postgres, Supabase), your codebase (GitHub), your tickets (Linear, Jira), your docs (Notion, Confluence), your support platforms (Intercom, Zendesk, HappyFox), your error tracking (Sentry), your billing (Stripe), and your CRM (HubSpot). Slack is the delivery channel so your team can ask from any thread.
Your support hire asks in plain English: "Why did Acme's usage drop?" Recon starts from the shared memory, refreshes from the live systems where the answer actually lives, writes and runs Python in an isolated sandbox when it needs to, and replies with cited evidence. No SQL, no engineering interruption.
Beyond on-demand investigations, Recon runs monitors on the accounts you care about, reflects daily across history to find patterns, and proposes advisories with evidence attached: create a Linear issue, prep outreach, start an investigation, watch a new pattern. You approve or reject. Memory compounds. Every week the brain knows more than it did the week before.
Integrations
Don't see one you need? Reach out and we'll prioritize it.
What we believe
Memory beats search
The most current answers live in your code, database, errors, and tickets. Recon reads from production reality directly and keeps a shared customer memory that compounds with every new signal, instead of asking the team to re-discover the same context every time.
The founder or engineer should not be the bottleneck
When a customer has an issue, the support hire should not wait hours for an engineer or the founder to dig through SQL. They should get the answer themselves, cited, in Slack, while the team ships product.
Investigation is the work
Ticket queues and reply tools are everywhere. Nobody was solving the 30 to 60 minutes of cross-system digging that lives between 'customer has an issue' and 'we know what's going on.' That is what Recon automates.
Production access is a responsibility
Recon connects to your database, codebase, errors, tickets, and billing. That access is read-only, sandboxed, cited, audit-logged, and gated by human approval for every write. Trust is a stack of small enforcements, not a single promise.
Who Recon is for
Any B2B SaaS team where customer questions keep routing back to engineering. Support, customer ops, sales, PMs, and founders who need real production context to answer a customer well, and engineers who would rather be shipping than writing the same SQL query for the tenth time.
If anyone on your team has ever Slack-pinged an engineer to ask "what is going on with this account," Recon is for you.
What's next
Discovery, investigation, monitors, daily reflection, and advisories are live today. The next frontier is depth: more integrations, richer pattern detection, and tighter feedback loops so the brain gets sharper with every approval and rejection.
Further out, the same shared memory becomes a customer-context layer your other agents can read from. Your coding agent fixing a Linear ticket asks Recon which customer is affected and how. Your sales agent prepping a renewal call asks Recon for the technical health story. Customer context only has to be learned once.
Who's behind this
Recon is built by Pratik, an engineer based in the Bay Area. Six years at AWS, then ten months at a B2B SaaS AI company watching the engineering team become a permanent triage layer for any customer question that needed production context. He left to build the system that solves it.
Building in public. Follow the journey on X at @chaibytesai.