Who this is for
AI Automation is built for any team spending real hours on work everyone already knows is repetitive. Most often: a senior person who keeps doing copy-paste because the system never quite gets it right, a junior person whose week is structured around reformatting reports, a partner who's still triaging email that could have been triaged by a rule. The shared symptom is that everyone involved knows the work is repetitive, and it stays manual anyway, because nobody has a spare week to fix it.
We're a fit when the goal is to compress decision time and remove low-judgment work, not to "add AI" for its own sake. Our discovery starts with the workflow, not the model.
Workflows we automate well
The automation patterns that pay back fastest, whatever business you are in:
Intake & onboarding pipelines
Forms, document collection, conflict checks, KYC, ID verification, file naming, folder creation, calendar invites, welcome emails, chained together so the AI can only fill in fields that already exist, never invent one. Human approval before any of it lands in your matter management system.
Report assembly & reformatting
Weekly or monthly reports built from spreadsheet data, CRM exports, and accounting platforms. The system pulls the underlying numbers, formats them per your template, and writes the narrative in your firm's tone. A human reviews and ships.
Email triage & routing
Inbound email classified into review, action, or archive, with extracted action items, suggested replies, and follow-up scheduling. Sensitive matters and judgment calls are always escalated to a human, never auto-replied.
Document review & comparison
Contract review where the AI flags deviations from a playbook, redlines them in track-changes, and writes a one-page summary. The deal team still reviews. The system removes the four hours of reading you did to find the three things that mattered.
Knowledge base & research agents
Retrieval-augmented agents trained on your internal documents, past matters, or sector-specific corpora. Answers carry source citations, and a question the corpus cannot support is flagged rather than guessed at.
Our automation approach
Three principles you'll see in every system we build:
1. A person approves anything that matters
It stops and asks youEvery step that touches money, identity, communication with a client, or anything legally binding stops and waits for a person. We turn that waiting off only when the firm asks and we've earned the confidence. Never as a default.
2. The AI fills in a form, it never writes freely
Fixed fields onlyEvery AI step produces a typed object (JSON or Pydantic), validated before downstream steps run. A "hallucinated" field that doesn't fit the schema fails the run and gets reviewed. It does not silently corrupt your database.
3. Vendor-neutral on models
No lock-inWe pick model providers based on capability and cost for your specific workflow, not because we have a reseller relationship. Most production systems we ship use a primary provider (OpenAI or Anthropic) with a secondary fallback, and an interface design that lets us swap providers in hours if you ever want to.
Tech stack
What we build with:
Python, FastAPI n8n / Make / native code Anthropic Claude OpenAI GPT-4o / o-series Pydantic / Zod schemas Postgres / Pinecone / pgvector Slack / Teams approval surfaces Cloudflare Workers AWS Lambda Langfuse / observabilityWe choose tools based on what your team can run and maintain after we leave. A no-code n8n workflow that your operations lead can edit is often a better answer than a custom Python service nobody can touch, and sometimes it is the other way around. The discovery decides.
After the build: monthly plans that keep it running
Automation drifts if nobody watches it. Once at least one workflow is live and working, you can pay us a flat monthly fee to look after it. The plan is called Managed AI Partner, and it comes in three levels, sized to how many workflows you run. One rule: we will not take money to maintain automation we have never seen run. The build always comes first.
AI Care
For one live workflow. We watch it for four things: steps that fail, the AI slowly drifting off-target, a model being retired by its vendor, and costs creeping up. We fix them within business hours, usually before you notice. Every month you get a plain summary of what ran, what broke, and what we changed.
AI Plus
For up to three live workflows. Everything in AI Care, plus we tune the prompts and rules when the output drifts or your business rules change, and your issues are answered first.
Operations
For connected systems of several workflows. Everything in AI Plus, with response times agreed in writing and review queues sized to your volume.
New automations are never folded into a monthly plan. Each one is quoted as its own build.
How a project runs
- A free 15-minute call. A quick look at your top workflow pains, so we can both tell whether a Discovery is worth booking.
- Scoping (1 week). Picked workflow gets a one-page brief: current state, proposed automation, model choice, where it stops for approval, how we will measure it, fixed price, target dates.
- Build (two weeks per workflow). Two-week delivery cycles with end-of-cycle demos. You see it running on your real data before we switch it on for good.
- Adoption (1 week). A training session, a written guide to how it works, and a one-page "what to do when it breaks" sheet for your team.
- Afterwards (optional). Put it on a monthly plan and we look after it, or take the keys and run it yourself.
Pricing
There are two parts, in a fixed order. The build is a fixed-fee project per workflow, scoped in writing after Discovery; a standard build runs two weeks. Managed AI Partner is the flat monthly fee that can follow it, available only once at least one workflow we built is live.
We do not publish prices. Every figure is set after Discovery, when we know the workflow, and confirmed in writing before anything is built. If you go ahead with the build within 14 days of your Discovery readout, what you paid for Discovery is credited in full against it.
Projects handling regulated data (HIPAA-adjacent, GDPR-heavy, data-residency requirements) are scoped for that in the same written quote. The AI itself bills separately: we pass through what the models actually cost, with the markup stated on the invoice.
Annual prepay is available on the monthly plans, with a discount stated in the quote. New workflow builds are quoted as separate projects, not absorbed silently into a retainer.
Guardrails: where it stops and asks you
- We will not build a fully autonomous agent that takes irreversible action on a client's behalf without explicit human approval. Not until the literature catches up with that promise; not on our watch.
- We will not deploy AI-generated decisions into legally binding contexts: pricing, contract acceptance, hiring decisions, regulated advice, without human sign-off.
- We will tell you, in writing, when an automation will save less than it costs to maintain. We are not paid by the workflow.