AI agents & workflow automation
We build agents that retrieve context, call scoped tools, and execute persistent workflows. Business rules, human approvals, and confirmed system writes turn model output into actions your team can trust.
The invoice, purchase order and receipt can disagree. Extraction identifies the fields; application code checks the numbers, permissions and proposed ERP action.
Inside the system
Follow a synthetic invoice from source evidence to a confirmed bill record.
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Parse the document into a schema while preserving page references. Invalid or ambiguous fields remain visible for review.
Resolve supplier and purchase-order references through scoped APIs. Keep the ERP snapshot separate from the document's claims.
Code calculates the variance; the model explains the discrepancy. Save the workflow state before an authorized reviewer decides whether the proposed action may proceed.
Execute the approved payload through a journaled adapter. After an uncertain response, inspect the existing operation and destination record before retrying anything.
Inspect the behavior
Three simulated runs show a confirmed match, a price exception and recovery from a lost write response.
Illustrative system · Synthetic inputsSynthetic INV-1043 matches PO-45018 at $8,100. The authorized AP reviewer has approved bill creation.
The approved $8,100 bill is read back from the simulated ERP. No payment action exists in this workflow.
Verification specification
We turn expected behavior into repeatable tests and operating controls. Acceptance thresholds are agreed for your workflow, data, and the consequences of an error.
Replay malformed documents and mismatched supplier references.
Unresolved required fields prevent a proposed write.
Restart the worker while a review is pending.
Resume the same durable state; recheck evidence freshness.
Lose the write response after remote acceptance.
Confirm the existing operation before any retry.
The handover
A defined implementation, reviewable evidence, and an operating model your team can own.
How we workTyped state, integration boundaries, allowed actions and approval rules.
Source evidence, proposed changes, durable checkpoints and operation receipts.
Replay cases, failure injection, deployment configuration and a runbook.
Architecture decisions
An agent can help when the next step depends on interpreting unstructured information or choosing between tools. We use deterministic code for calculations, policy checks, and predictable transactions. Multi-agent orchestration is appropriate when tasks benefit from separate responsibilities and can be evaluated independently.
We assess APIs, webhooks, authentication, and rate limits, then define the permitted reads and writes. Where appropriate, Model Context Protocol (MCP) exposes tools through a shared interface. We design retries and idempotency so a repeated request does not create a duplicate transaction.
Talk to an engineer
Bring a workflow, a product brief, or an AI system that needs to ship.
