Pipelines thathandle their ownbad days.
Self-sustaining data operations where agents absorb the routine failures that used to page a human, and escalate the ones that genuinely need judgement.
- 16
- agents in one production system
- ~70%
- faster customer onboarding
- 24/7
- unattended operation
Data teams spend most of their week on work that is urgent, repetitive and beneath them: a schema changed, a file arrived late, a vendor swapped a column name. Agents are good at exactly that class of problem. We put them where the toil is, give them tightly bounded authority, and make every action they take auditable and reversible.
The work itself.
- 01
Agentic pipelines
Ingestion and transformation that adapts to upstream change instead of failing and waiting for a human to notice.
- 02
Self-healing operations
Detection, diagnosis and remediation of the routine failure modes, with a clear escalation path for everything else.
- 03
LLM orchestration
Multi-agent systems with defined roles, explicit handoffs and hard limits on what each one is permitted to do.
- 04
Data unification
Identity resolution and enrichment across sources, so a customer is one record rather than six near-matches.
- 05
Activation
Governed reverse ETL into the operational tools where the data changes what someone actually does.
- 06
Audit and control
Every agent action logged, attributable and reversible, with spend caps and permission boundaries enforced in code.
Four phases, and whatyou hold at the end of each.
- 01
Toil audit
We instrument the current operation and count where the hours go. Agents get pointed at measured toil, not assumed toil.
You getRanked toil inventory, automation candidates
- 02
Bounded pilot
One agent, one failure class, running in shadow mode beside your team until its judgement matches theirs.
You getShadow-mode agent, agreement rate against humans
- 03
Supervised autonomy
The agent acts, a human approves. Approval thresholds relax as the record earns it, never on a schedule.
You getLive agent, approval workflow, audit log
- 04
Fleet
Additional agents and the orchestration between them, with the control plane, spend caps and kill switch in place.
You getMulti-agent system, control plane, runbooks
What we build on.
We are not tied to any of these. The choice follows what you already run and what your team can maintain once we are gone.
- Claude / GPT
- LangGraph
- Model Context Protocol
- Dremio
- Snowflake
- dbt
- Hightouch
- Temporal
- Fivetran / Airbyte
- AWS / Azure / GCP
Shipped, and still running.
- Clear CompanyAI Automation & Customer 360°Provisioning Automation + Customer Cortex — customers live in days instead of weeks with real-time health, churn and upsell intelligence.Read case study
- MedlineSCOUT — Agentic AI for Accounts PayableAutonomously resolves shipping discrepancies across SAP, AS400, MPower and more — closing a 6,000-ticket backlog while absorbing 283 new tickets a day.Read case study
What people ask first.
Ready to buildwhat comes next?
Tell us about the system, product or intelligence layer you need running in production.
- LinkedInNuaav
- Phone+1 703 462 3363
- Address42346 Ponderosa Drive,
Chantilly, VA 20152