Most corporate AI initiatives produce little more than an ungrounded chatbot that invents facts and terrifies legal counsel. A toy model guessing at your company's records is a liability, not an asset. I build enterprise AI integrations designed around how serious businesses actually function: strictly grounded in your private documents through retrieval-augmented generation (RAG), cordoned off by role-based data permissions, and governed by deterministic human-in-the-loop approvals before any permanent change is made to your database or customer accounts.
Discuss your projectThe final scope is agreed around your project.
We cut through the AI hype to identify where language models actually create leverage—extracting invoices, summarizing case files, triaging client inquiries—and where plain deterministic code is safer and cheaper.
Your assistant references your actual internal manuals, contracts, and operational guides via secure vector search (pgvector). It answers with source citations rather than making educated guesses.
Transform messy PDFs, customer emails, scanned receipts, and legacy forms into clean, validated JSON records that feed directly into your database without manual transcription.
Equip the model with typed, deterministic function calls—allowing it to query inventory, calculate shipping, or draft calendar entries through approved API boundaries rather than freeform text.
Strict tenant and user isolation. A junior employee or external customer chatting with the assistant can never access executive payroll, confidential contracts, or unapproved records.
High-consequence actions—issuing refunds, sending client-wide emails, updating contracts—are staged as drafts requiring explicit human verification before execution.
Every prompt, completion, tool call, and human approval is logged with latency and token cost tracking. We benchmark model accuracy against real historical test cases before shipping to production.
Whether an embedded copilot sidebar, an interactive modal, or an asynchronous email processor, the assistant feels like a native, beautifully styled extension of your existing application.
We cut through the AI hype to identify where language models actually create leverage—extracting invoices, summarizing case files, triaging client inquiries—and where plain deterministic code is safer and cheaper.
I engineer AI systems using the right model for the right task—leveraging Claude (Anthropic), OpenAI, or local open-weights models (via Ollama or vLLM) based on privacy, latency, and cost requirements. Knowledge is indexed using PostgreSQL with pgvector for fast hybrid semantic search. Tool integrations and agentic routines are implemented using type-safe TypeScript interfaces with strict schema validation (Zod), ensuring the model never passes malformed parameters to your production systems.
No. Never. We exclusively use commercial enterprise API agreements (such as Anthropic and OpenAI enterprise endpoints or dedicated self-hosted open models) where data retention is strictly zero and vendor model training on customer payloads is explicitly prohibited by contract.
By anchoring the model strictly to your verified documents through Retrieval-Augmented Generation (RAG). Rather than asking the model to answer from general training memory, we retrieve the exact relevant passages from your private database and instruct the model to answer only using those sources. If the answer cannot be found in your approved files, it is programmed to state that clearly and offer human escalation.
No. The system is engineered with strict human-in-the-loop guardrails. Read operations can be automated for instant answers, but any write or mutation operation (updating a customer status, issuing a payment, sending a contract) is staged as a pending action in your dashboard, awaiting a human operator's one-click approval.
Surprisingly little when engineered thoughtfully. By selecting lightweight, fast models for initial document routing and extraction, caching vector embeddings, and invoking heavy reasoning models only when truly necessary, typical business workflows cost pennies per hundred transactions. We build in real-time token budgeting so there are never unexpected monthly surprises.
Yes. It is designed specifically to integrate into existing platforms. Whether you need an interactive copilot panel inside your current Next.js or React app, an automated webhook worker that processes incoming emails in the background, or a Slack/Teams integration for your internal team, it hooks directly into your current tech stack.
Every engagement is quoted individually, based on its scope, complexity, and delivery needs. We agree on the work and its cost before development begins.
For bespoke projects, you own the custom code, with client-controlled repositories and infrastructure, documentation, and a complete handover. Third-party components and services retain their own licenses and terms. When an existing product fits your needs, I can help you adopt and configure it, avoiding unnecessary development. You receive access under that product's agreed terms; its underlying platform remains with its owner.
One-off projects include 60 days of bug fixing and stabilization after launch for the agreed delivery. Continued support can follow through a maintenance agreement, with additional features scoped separately. Existing-product access follows that product's support terms.
Tell me about the idea, the problem, or the part of your product you want to move forward. We can work out the scope and the right next step together.
Let's talk about your product