Agentic workflow design
We map the real process first — including the exceptions people handle informally — then decide what an agent should own, what stays deterministic code, and where a human has to sign off.
Agentic workflows that take real work off human hands.
AI Automation
Most teams do not need another dashboard. They need the twenty repetitive steps between an incoming request and a finished outcome to happen without a person shepherding them. We design and build agentic workflows that read the inputs, call the systems you already run, make the routine decisions, and escalate the rest to a named human. Every workflow ships with an evaluation suite, an audit trail and a cost ceiling, so you can see exactly what it did and what it spent.
What you get out of it
- Manual, repetitive operational steps executed end to end without a person driving them
- A reviewable audit trail for every automated decision, including the inputs it acted on
- Human approval gates on the actions where a wrong answer is expensive
- Predictable per-run cost and latency, monitored and capped rather than discovered on the invoice
Capabilities
What the work involves
The concrete engineering that makes up a ai automation engagement.
Document and data pipelines
Ingestion, parsing and extraction for invoices, contracts, forms, tickets and exports. Structured output validated against a schema, with low-confidence records routed for review instead of silently guessed.
Internal operations automation
Triage, routing, reconciliation, status chasing and reporting. The unglamorous work that consumes a team’s week and never appears on a roadmap.
Human-in-the-loop approvals
Approval steps that live where people already work — Slack, email, or a small internal UI — with the agent’s reasoning, the source documents and a one-click approve, edit or reject.
Integrations with your existing stack
Slack, Jira, GitHub, HubSpot, Salesforce, Zendesk, Notion, Google Workspace, ERPs and internal APIs. We integrate with what you run today rather than asking you to migrate first.
Evaluation, guardrails and cost control
A labelled test set per workflow, regression runs on every change, schema and policy validation on outputs, and per-run budget limits with model routing to keep spend flat as volume grows.
Deliverables
What lands in your repository
Everything we produce is yours, in your accounts, documented well enough for your own engineers to carry forward.
- A process map of the current workflow with the automation boundary drawn explicitly
- Production workflow code in your repository, with infrastructure defined as code
- An evaluation suite and labelled test cases you can run in CI
- Observability: traces, per-step logs, cost and latency dashboards, failure alerts
- Approval interfaces for the human checkpoints, in the tools your team already uses
- A runbook covering failure modes, rollback and how to extend the workflow
Technology we reach for
Chosen per project against your constraints and what your team can maintain — never because it is new.
- Python
- TypeScript
- LangGraph
- Temporal
- OpenAI
- Anthropic Claude
- Postgres
- Redis
- Celery
- Docker
- AWS
- Terraform
Questions
AI Automation, answered directly
The best candidates are high-volume, rules-heavy processes where the inputs are messy but the desired output is well defined — invoice handling, ticket triage, data reconciliation, compliance checks. If a process is fully deterministic, plain code is cheaper and more reliable than a model. If it happens twice a month and takes ten minutes, the automation will cost more than it saves. We assess volume, variability and the cost of an error before recommending anything.
Three layers. First, we constrain what the agent can do: narrow tools, scoped credentials, and schema validation on every output. Second, we place human approval gates in front of irreversible actions such as payments, external emails or production writes. Third, we run an evaluation suite against labelled cases on every change, so a regression is caught before deployment rather than in production. Everything is logged and reversible where the underlying system allows it.
No. Automation is most valuable when it sits on top of the systems your team already trusts. We integrate through existing APIs and webhooks — Slack, Jira, GitHub, your CRM, your ERP — and the people doing the work keep their current interfaces. If a system has no usable API we will say so early and propose an alternative path rather than building something fragile on top of screen scraping.
We measure cost per run from the first prototype, not after launch. Techniques include routing simple steps to smaller models, caching repeated context, trimming prompts to what the step actually needs, and running deterministic code wherever a model is not required. Each workflow gets a per-run and per-day budget ceiling with alerting, so volume growth shows up as a dashboard line rather than a surprise invoice.
Next step
Tell us what you are trying to build.
Send us the problem, the constraints and the deadline. We will come back with a technical approach, a shape for the team, and an honest view of what is achievable.