Agentic workflows
that finish the job.
Multi-step agentic DAGs that orchestrate humans, models and APIs. Invoice processing, onboarding, support triage, contract review — straight-through where confident, human-escalated where not.
Built for production, not just demos.
- Visual DAG designer + as-code SDK (TS + Py)
- Agentic reasoning with tool use and verification
- Human-in-the-loop steps with audit log
- Document understanding (OCR + KIE + extraction)
- ERP / CRM / ticket / identity integrations
- RPA fall-back for unstructured legacy systems
- Conditional routing, retries, idempotency, replay
- Live throughput + straight-through + cost dashboards
How a typical engagement runs.
Map
Trace the current process — actors, decisions, exceptions, SLAs.
Design
Build the agentic DAG with tools + human steps + escalation rules.
Integrate
Wire ERP + CRM + identity + ticket; map fields to your schema.
Pilot
Run shadow-mode on real traffic; tune confidence + escalation thresholds.
Operate
Production with live dashboards + drift + cost monitoring.
What you get in your bucket.
Questions, answered.
Is this LangChain / LangGraph?
We support LangGraph but typically deploy our own runtime — built for production reliability with replay, idempotency and operator observability.
How is this different from RPA?
Agentic workflows reason and adapt; RPA is brittle to layout changes. We use RPA as a fall-back leg when the system is too legacy for an API.
Can we keep humans in the loop?
Designed for it. Any step can require human approval, dispatch to a queue, or escalate based on confidence.
How do you handle compliance?
Per-step audit log, replay, role-based access, immutable storage — meets SOX, HIPAA, GDPR + DPDP requirements depending on tenant.
Ready to build
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