Service / AI
Automate the work between your systems.
End-to-end automation of document-heavy, rules-heavy processes, where AI reads and drafts and your systems of record stay the source of truth.
[ 01 ]The short answer
What is AI Workflow Automation?
AI workflow automation connects language models to the systems your team already uses, CRM, ERP, email, ticketing and spreadsheets, so that reading, classifying, drafting and updating happen without manual copying. Unlike rule-only automation, it handles unstructured inputs such as emails and PDFs. We design each workflow with a clear owner, retries and idempotency, and an exception queue, so the automation is boringly reliable and people only see the cases that need them.
What you get
- Workflow map with owners and SLAs
- Production workflows with retries and idempotency
- Integrations to your systems of record
- Exception queue and reviewer UI
- Weekly impact report
[ 02 ]What we build
The pieces, built to last.
- 01
Inbox and document intake
Emails, attachments and uploads read, classified and turned into structured records.
- 02
System integrations
Typed connectors to Salesforce, HubSpot, NetSuite, SAP, Google Workspace, Slack and your own APIs.
- 03
Durable workflows
Steps that retry safely, never double-post and pick up where they stopped after a failure.
- 04
Exception queues
Low-confidence cases routed to the right person with the context already gathered.
- 05
Scheduled agents
Follow-ups, reminders and reconciliations that run on a timer and report what they did.
- 06
Measurement
Time saved, error rates and cost per case, reported weekly, so the business case stays honest.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
Accounts payable
Invoices matched to purchase orders and receipts, with mismatches flagged instead of paid.
- 02
Customer onboarding
Forms, IDs and contracts checked and entered, with missing items chased automatically.
- 03
Claims and cases
Incoming claims summarised, triaged and assigned with the evidence attached.
- 04
Revenue operations
Leads enriched, deduplicated and routed, and follow-ups sent on time, every time.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- Temporal
- Inngest
- n8n
- Cloudflare Workers
- Supabase
- Postgres
- Resend
- Claude
- GPT
- Groq
- Zapier where it fits
[ 07 ]Further reading
Problems we found, and how we would solve them.
- AI agents3 min read
Why AI agent pilots stall before production
ProblemAgent pilots impress in demos and stall before production because nobody can show they work, limit what they can do, see what they did, or overrule them.ProposalDraw a hard decision boundary: the model scores and drafts, deterministic code decides and acts, every step is logged with its reasoning, and a person can overturn any decision in one click.Read the proposal - Cost3 min read
Your AI bill is a routing problem
ProblemAI applications send every request to a frontier model with a long prompt, so cost and latency grow with usage while most requests never needed that capability.ProposalRoute each request to the cheapest model that passes your evaluation for that kind of request, cache shared prompt prefixes, batch what can wait, and distil a small model for the highest-volume task.Read the proposal
[ 08 ]FAQ
Questions buyers ask.
RPA replays clicks and breaks when a screen changes. We integrate through APIs, and AI handles the unstructured parts that rules cannot, such as reading an email or a scanned invoice.
Each step has a confidence threshold set in code. Below it, the case goes to a person with the context attached, and their decision becomes a test case.
A first workflow usually runs on real cases within four to six weeks of the deep dive, then we widen coverage.
[ 09 ]More AI services
- Custom AI Model DevelopmentA model trained on your data, owned by you.
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Voice AI AgentsEvery call answered, qualified and logged.
- Document AI and Intelligent Document ProcessingPaperwork turned into clean, checked data.
- Private LLM DeploymentAI on your infrastructure, data never leaves.
- LLMOps, Evaluation and GuardrailsKnow your AI works before customers find out.
- Predictive ML and ForecastingDecisions made on what will happen next.
One company at a time