Who this is for
- A concrete text-heavy process: intake, triage, research, reporting
- A human owner who will review the first version
- Models and a gateway you already run, or are about to
Who this is not for
- “Build us an autonomous employee”
- Unrestricted customer-facing chat with no eval set
- Multi-agent swarms as the starting point
What we deliver
- Workflow discovery and design mapped to a concrete process and success metric
- Evaluation harness first — frozen samples, pass/fail, not vibes
- Agent and pipeline development with guardrails, retries, and human-review checkpoints where they matter
- Integration with your existing tools: email, ticketing, CRM, databases, file stores
- Documentation and operator training for your team
How an engagement runs
- Pick one process. One workflow with a measurable outcome. We will not boil the company.
- Harness. Frozen samples and a pass bar before we write tools.
- Build. Permissions, schemas, HITL. Runs against your models / gateway.
- Ship behind a flag. Roll out on real traffic only after the harness stays green.
Stack we actually use
- your LLM gateway
- constrained / schema outputs
- eval harnesses
- n8n or equivalent only if it earns it
Tested on our fleet
Dated lab notes — not a client logo wall.
Related services
Common questions
What kinds of workflows benefit most from AI automation?
High-volume, pattern-rich, text-centric processes: document intake and extraction, drafting and summarizing, triage and routing, research and monitoring, internal Q&A over your own knowledge base. If humans spend hours reading and writing structured text, it's a candidate.
Can workflows run fully on our own infrastructure?
Yes. We build against self-hosted models wherever feasible, keeping documents and data inside your network — with cloud APIs as an optional escape hatch only where you allow it.
How do you prevent AI errors in production workflows?
Layered defenses: constrained outputs, validation against known schemas, confidence thresholds, targeted human review, and continuous evaluation on real samples. Automation is rolled out gradually per process with measurable error rates.
Agent vs a plain workflow?
Most first jobs are pipelines with a model in the middle. We reach for an agent only when the process has to choose tools mid-flight. Default is the simpler thing.
Do you use LangChain, Crew, or multi-agent frameworks?
Not by default. Framework gravity is real. We add one when a constraint requires it and say so in the proposal.
How do you measure success?
Task pass rate on a frozen sample set, plus the operational metric for that process (time-to-triage, extraction accuracy, review load). If we cannot name both, we are not ready to build.