AI Systems & Operational Middleware
Custom AI middleware, automated CRM workflows, and multi-system integrations — built so your tools stop fighting each other.
Data Ingestion Layer
HubSpot / Salesforce / Pipedrive
Webhooks, Stripe, Email, Forms
Spreadsheets, PDFs, Voice memos
Complex extraction, deep reasoning, nuanced synthesis
High-volume triage, fast classification, enrichment
Deterministic schema verification (Zod), deduplication, hallucination traps, and manual review triggers.
Automated retries with exponential backoff, rate-limit shielding, and complete immutable audit logging.
Production Execution Layer
Normalized CRM deals, contacts & notes
Slack alerts, Linear tickets, email queues
Postgres, Supabase, analytics pipelines
Scope
Production-grade operational plumbing connecting your data, your models, and your revenue engines.
Claude and Gemini routed to the job each is actually good at, not one model bolted onto everything.
Records that update themselves across your tools, with the safety rules that stop bad data spreading.
The old system that works but doesn't talk to anything, wired cleanly into the rest of your modern stack.
“We stop your CRM, email, and spreadsheets fighting each other so you can stop copy-pasting data like it's 2004.”
Production Systems
Verifiable operational middleware, autonomous pipeline architectures, and shipped AI products.
AI briefs arrived ambiguous — "build something with AI" — with no defined inputs, outputs, constraints, or success metrics, and no clarity on whether a given feature should be a copilot a person drives or an agent that acts on its own.
A fast-growing outbound sales agency — running campaigns for 7 enterprise clients, scaling toward 30–40 within a year — was operating entirely out of drifting spreadsheets. In the baseline client dataset alone: only 4 of 1,259 phone numbers were in a usable format, 359 rows had the country typed into the city field, 64 of 248 companies didn't match any approved target list, and there was no unified opt-out enforcement across clients — a real compliance risk, not just a tidiness problem.
Staff onboarding and day-to-day workflows had usability issues the teams using them had come to treat as permanent — the kind of friction people stop reporting because they've stopped expecting it to change. Nobody had gone back to actually fix the root causes.
Execution Process
Diagnosis before prescription. A disciplined four-stage gateway model so you never pay for unnecessary builds.
20 minutes, one workflow, verbal. An honest diagnosis on where AI would earn its place — and where it wouldn't.
The diagnostic. Your workflows and data foundations mapped end to end, showing what to fix before automating.
The custom middleware, webhooks, and multi-model routing pipelines, built and tested against live data.
Ensuring the team actually uses it, edge cases are caught, and the system holds reliably after handover.