AI can produce more code than people can coordinate. tam is a delivery platform for AI-first engineering teams: it unifies task management, project knowledge and AI coding agents into a single, governed delivery loop — run by a dedicated delivery agent.

🔗 thetam.app

Product tam — AI-native software delivery platform

For CTOs and Tech Leads of AI-active engineering teams

Role Full-stack developer (core team)

Timeline 2026 — ongoing

Stack React, NestJS, PostgreSQL / Prisma, Redis, Socket.IO, MCP, Next.js, Kubernetes

AI generates code instantly — but feature delivery hasn't kept pace.

Instead of shipping high-impact work, senior engineers spend their days on supporting activities: supervising agents, updating tickets and managing handoffs between people and AI. The bottleneck has moved from writing code to coordinating it.

tam is a delivery platform powered by a dedicated delivery agent. The agent breaks goals down into a single, minimal-delay delivery loop for humans and AI agents — handling task handoffs, approvals and automated quality checks, so the team moves from goal to milestone without the "work about work".

People and AI teammates work as one team. The platform plans, coordinates and keeps track of what each of them does.

ComponentWhat it doesTask trackerIssues, sprints and visual boards — Scrum or Kanban, your statuses, your T-shirt estimatesProject knowledge baseWiki and docs that hold team context and guidelines — for people and agents alikeDelivery agent @tamThe orchestration engine: plans, coordinates and syncs approved work across human roles (Dev, QA, PM) and AI teammatesAI teammates via MCPClaude Code, Cursor, ChatGPT and agent orchestrators join the loop through the Model Context Protocol

Automated workflow coordination — no more ticket-updating overhead or manual handoffs between humans and AI agents

AI teammate orchestration — bring AI coding assistants into the core loop as full-fledged team members, with dedicated permissions, audit logs and secure access

Faster release cycles — automated evidence compilation (Evidence Bundles) and 1-click execution approvals

@tam gathers project context, coordinates specialist work, stops before changes and leaves a reviewable record of the result.

AI in the delivery loop is only useful if it's safe. tam is built around control:

Permissions like a new hire — every AI teammate gets its own name, role and narrow scope; access is granted gradually and revoked in one click

Plan before action — the agent proposes, a human approves the exact change

Evidence, not promises — every agent step leaves an auditable record

Fail-closed security — unknown tools, scopes or revoked credentials are rejected by default

You lose nothing — you just do less by hand. The board, tasks and docs stay where they are: drop in anytime to steer the work, check progress, or just see what's happening.

Available now: task tracker, knowledge base, MCP integration with AI coding assistants, personal dashboard, real-time collaboration, Global (EN) and CIS (RU) editions with self-service billing

Closed beta: the @tam delivery agent and AI teammates — onboarding 5 design-partner teams, targeting a 30% shorter time-to-milestone without sacrificing code quality

Planned integrations: GitHub, Notion, Slack, Telegram, analytics

A production-grade, multi-tenant platform engineered for both human teams and AI clients.

MCP server — local stdio and Streamable HTTP; a single generated registry of 160+ typed tools drives backend route policy, MCP validation, consent UI and docs

OAuth for AI clients — PKCE (S256), tokens bound to workspace, project and tools; rotation, reuse detection and revocation

Horizontally scalable backend — multiple API pods, background workers, DB-backed job locks, Redis-backed realtime fanout

Regional model gateway — stateless service for LLM provider calls, separated from product logic

Quality gates — hundreds of unit and real-dependency E2E tests across backend, frontend and MCP, browser regression journeys, CI on every change

Operations — Docker, Kubernetes, Prometheus metrics, Sentry, documented rollback policy

LayerTechnologyWeb appReact, Vite, TypeScript, Tailwind CSS, Radix UI, TanStack Query, Zustand, Socket.IO clientBackendNestJS, Prisma, PostgreSQL, Redis, pg-boss, Socket.IO, StripeAI integrationModel Context Protocol (MCP) server, OAuth 2.1 / PKCE, model gatewayPublic siteNext.js, next-intlInfrastructureDocker, Kubernetes (k3s), S3 / MinIO, Prometheus, Sentry, GitHub ActionsTestingJest, Playwright, full-stack E2E

Full-stack work across the web app, backend, MCP server and public site:

Personal dashboard ("My work") — read contract, UI, optional modules, attention ranking, access-aware @tam threads

Notifications — personal notification system

Issues — comments with image and file attachments, subtasks, custom fields, history, search by issue key, watch / follow

Project labels — label registry replacing free-text tags

MCP server — real error codes and field coverage for AI clients

Onboarding tour, skills and billing UI overhaul

Public site — landing updates, SEO / GEO, blog launch with 25+ articles on AI agent governance, MCP docs reference, partners and closed-beta design-partner pages

Leading an AI-active engineering team? Join the closed beta → thetam.app