These aren't features I'd build from scratch for you. They're vetted, production-tested components I've already shipped and reuse across systems. Each one is a business outcome first and an engineering pattern second — and every build contributes at least one component back, so the library, and your advantage, compounds.
A self-hosted replacement for Zapier, Make, and n8n — the same connect-this-to-that automation, running on your own infrastructure with no per-task fees and no data leaving your control.
Mechanism: a visual workflow builder over a connections hub, with retries, deduplication, and recovery built into every step.
A curated matrix of every model worth using, scored on price, benchmark performance, and task affinity — so each job runs on the cheapest model that meets the quality bar, not a one-size-fits-all default.
Mechanism: a human-curated routing table as single source of truth, audited weekly by an automated job; no model strings hardcoded anywhere.
Instead of guessing which model is best for a task, the system tries them and learns — but won't crown a winner on a lucky streak. A model has to earn its dominance on real data before it gets the traffic.
Mechanism: Thompson sampling for selection, gated by a Wilson lower-bound confidence check (n ≥ 25) before any model can dominate.
Sales calls, client calls, discovery calls — captured, structured, and turned into concrete action items and follow-ups automatically, so nothing gets lost in a recording nobody re-watches.
Mechanism: call transcript → structured extraction → action items and next-steps, wired straight into your task system.
Every AI call in your business routed through one controlled door — tagged, metered, budget-capped, and security-screened — so you always know what you're spending and nothing untracked ever runs.
Mechanism: a LiteLLM proxy with hard gate hooks — secret scanner, injection canary, token-per-dollar floor, provider circuit-breaker, and a per-build budget guard that blocks at 100%.
An open-source intelligence and recon engine that reads a live build the way an attacker or auditor would — dependencies, secrets, architecture drift — and reports what it finds with honest, source-clamped confidence.
Mechanism: passive OSINT across dependency CVEs (OSV / EPSS / KEV), secret scanning, and intended-vs-actual architecture reconstructed from git history.
Hand it a half-formed idea and it interrogates the gaps, takes a position on what to build (and what not to), and maps the whole journey — the way a senior strategist would, not a form that spits back your own inputs.
Mechanism: a multi-pass loop — interview → diagnose → adversarial self-critique → refine — using a stronger model on reasoning steps and a cheap one on extraction.
Scoping and pricing that splits human time from AI time, accounts for what's reused, and will not quote below what the build actually costs — so the number is disciplined, not optimistic.
Mechanism: a modality-split effort model + reuse credit from the component registry + a cost-floor hard-stop, calibrated in shadow mode against real builds first.
A 28-engine orchestration OS and the supporting systems that keep it safe, observable, and cheap. A selection of what's under the hood — each one built, running, and reused across systems.
Per-call model selection that learns: Thompson-sampling bandit + CUSUM drift breakers + quality-weighted reward, so routing improves itself.
Five adaptive loops plus a nightly self-reflection cycle — the system reviews its own behavior overnight and measurably gets smarter.
A 9-stage board run by four autonomous agents — drafter, worker, QA reviewer, planner — that ship work end to end.
The work-routing core that triages every task by stakes, urgency, and reversibility before it reaches a human.
Crash-resume, retries, and exactly-once delivery — Temporal-grade run-safety at the cost of just the tokens. Replays never double-send.
Trigger → steps → outputs, built from a form. A Zapier/Make-style surface with a manual dry-run and a global kill-switch.
One registry that wires Slack, scheduling, call data, and CRMs together — status by presence, secret values never exposed.
One AI analysis writes structured rows once; every dashboard reads from SQL. Scores calls against the client's real methodology.
Polls failures continuously, fixes the root cause, verifies the fix, and teaches the check to stop re-firing — so alert classes shrink over time.
Catches quality degradation and hallucination on live output — and can auto-spawn an experiment to correct it.
Automated A/B tests with Bayesian gates that auto-revert any change that drops quality, drifts, or hallucinates.
One pane over 259 automation surfaces with 99.6% kill-switch coverage — total operational visibility and one-switch control.
Every output graded on seven axes, feeding the routing reward so the system favors what actually works.
Edits to the scoring rubric run a golden-set eval and auto-revert on regression — the IP can't silently degrade.
Full-text search + persistent user facts + session memory, write-time-summarized and feeding every prompt.
Swapping the database (SQLite → Postgres) is a config flip with contract-tested parity, not a rewrite.
Every task priced against a role-matched human baseline — often 1,000× cheaper — under a hard $250/mo governed budget.
A structured expert brief and an independent expert verdict before any risky or irreversible production change.
A named behavioral doctrine every agent inherits and is checked against in QA — alignment without fine-tuning.
Agents earn autonomy like staff, auto-demote when rejection rates climb, and can be paused instantly.
A secret scanner, a prompt-injection canary, a provider circuit-breaker, and a budget guard that blocks at 100% — on every call.
Clients connect their own accounts (e.g. QuickBooks) with per-tenant encrypted, isolated tokens and audited connect/refresh/revoke.
Secrets can't be committed, malformed jobs are rejected at the door, and two builders can't deploy on top of each other.
A live system map that introspects what's actually running — dead components render cold and front/back-end drift is flagged, so the docs can't rot.
Every role sees only what it must act on — "no insight without a next action" — over one shared data spine.
Makes the whole platform a native Claude / Cowork connector — no token paste, scoped read-only by default, with instant secret rotation.
Extra workflows on the same engine: reusable marketing assets pulled verbatim from call transcripts, and weekly sourced competitive intel.
One AI assistant configured into many bots — built once, runs a coach bot and a tech-support bot from the same core.
Unit, end-to-end, and production smoke tests — idempotency replay, failure modes, role scoping, and auth guards — on every change.
Behind these capabilities sit a 28-engine platform and 33 documented patterns, each benchmarked against published research. Every build checks the registry before writing anything new and contributes at least one generalized component back when it ships, so each project starts further ahead than the last. The tools are buyable; the accumulated, vetted, reuse-ready library is not.
Tell me the outcome you're after — I'll tell you which modules get you there and what's net-new.