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Services

We do not sell seats on a platform you have to rent. We take a specific, expensive, manual process and we automate it — then we hand you the keys.

01

AI Agents

We build LLM-powered agents that read the request, use your tools, and finish the job — with evaluation harnesses and human-in-the-loop checkpoints so output stays trustworthy at volume.

Typical outcomes

  • Cut manual review time by 60–90%
  • Automate triage, classification, and routing
  • Escalate to a person when confidence is low
  • Ship with evals, guardrails, and audit trails

What you get

  1. 01Use-case discovery & ROI model
  2. 02Agent + tool-calling implementation
  3. 03Evaluation suite and regression harness
  4. 04Human-in-the-loop review console
  5. 05Run logs and audit trail

Tools we reach for

ClaudeOpenAILangGraphMCPTemporalModal
Discuss AI Agents
02

Workflow Automation

Most lost hours live between tools, not inside them. We map the real process, then replace the copy-paste with durable, observable integrations that recover from failure on their own.

Typical outcomes

  • Eliminate recurring manual data entry
  • Sync CRM, billing, and ops in real time
  • Durable retries instead of silent failures
  • Full visibility into every run

What you get

  1. 01Process mapping & bottleneck analysis
  2. 02Integration architecture
  3. 03Durable workflow implementation
  4. 04Alerting, dashboards, and SLAs
  5. 05Ops documentation

Tools we reach for

Temporaln8nZapierSegmentAirbytePostgres
Discuss Workflow Automation
03

Document Intelligence

Intake is where most operations stall. We build pipelines that read the documents your business runs on, extract what matters, check it against your rules, and answer questions from your own knowledge.

Typical outcomes

  • Clear intake backlogs without adding headcount
  • Answer internal questions from your own documents
  • Flag exceptions instead of reviewing everything
  • Trace every answer back to its source

What you get

  1. 01Document and data audit
  2. 02Retrieval / RAG architecture
  3. 03Extraction and validation pipeline
  4. 04Exception review queue
  5. 05Accuracy reporting

Tools we reach for

ClaudeOpenAIpgvectorPostgresPythonModal
Discuss Document Intelligence
04

Custom AI Systems

When off-the-shelf tooling forces your team into someone else's workflow, we build the system that fits — the operator consoles, permissions, and unglamorous plumbing that let automation hold up in production.

Typical outcomes

  • Replace brittle spreadsheet workflows
  • Consolidate 4–5 SaaS tools into one system
  • Role-based access your compliance team signs off on
  • A codebase your in-house team can own

What you get

  1. 01Technical discovery & architecture doc
  2. 02Data model and API design
  3. 03Production application + admin tooling
  4. 04CI/CD, observability, and runbooks
  5. 05Handover, docs, and team enablement

Tools we reach for

TypeScriptNext.jsPostgresPrismaAWSTerraform
Discuss Custom AI Systems

Questions

The things everyone asks.

If yours is not here, ask it on the call — we would rather answer it early than have it surface in week six.

How much does an engagement cost?

It depends on what we are automating, so we cover pricing on the first call, once we understand the process and what it is costing you today.

How quickly can you start?

Discovery calls happen within a few days. Depending on current capacity, new engagements typically kick off within two to three weeks.

Who owns the code?

You do — outright, on your infrastructure, in your repositories, from the first commit. No licensing arrangements, no proprietary runtime you have to keep paying for.

What happens when the AI gets something wrong?

Every automation is built around that question. We test against your real cases before launch, set confidence thresholds that send uncertain work to a person, and log every run so mistakes can be traced and corrected.

What happens to our data?

Automations run in your accounts, under your keys. During the blueprint we document which data reaches which model provider, so your security team can review it before anything ships.

What if AI is not actually the right answer?

Then we will tell you that in the diagnostic. A meaningful share of "we need AI" requests are better solved by fixing a data model or automating a handoff. We would rather lose the bigger project than ship something you regret.

What happens after launch?

Every engagement includes a 30-day support window. Beyond that we offer retainers for ongoing iteration, or a clean handoff with documentation and paired sessions if you are taking it in-house.

Next step

Bring us the ugliest process you have.

The one held together by a spreadsheet and one person's memory. That is usually where the biggest return is hiding.

Typical reply within one business day