Production AI Engineering
We architect, build, deploy, and harden AI systems that must work in the real world—from data and model integration through workflows, controls, evaluation, and production operations.
These are the moments when AI moves from exploration to real engineering. We help teams turn these inflection points into reliable, production-grade systems.
The system just went live, now reliability, state, and controls become real engineering problems.
The demo works, but production readiness, integration, and operational trust are blocking rollout.
A failure, misfire, duplicate action, or broken downstream state exposed the need for recovery and traceability.
Security, permissions, auditability, policy boundaries, and governance now have to be engineered into the system.
The company needs senior production AI engineering capacity to harden and operationalize what already exists.
Capabilities
Once AI changes real systems, correctness includes authority, state, side effects, recovery, observability, and business outcomes. We engineer for all of it.
Least-privilege execution across actors, policies, approvals, and real system boundaries.
ALLOWACTOR · AGENT · BILLINGLIMIT · THRESHOLDSPersistent changes, external calls, transactions, and downstream impact made explicit.
PAYMENT · REFUNDEDORDER · UPDATEDLEDGER · BALANCEDRetries, idempotency, rollback, repair, and reconciliation engineered for partial failure.
ATTEMPT 01 · TIMEOUTATTEMPT 02 · SUPPRESSEDRECONCILE · COMPLETEDecision, tool, mutation, and outcome evidence captured as one operating trace.
DECISIONMUTATIONOUTCOMEAI behavior tied to measurable business invariants, operating constraints, and durable results.
authority heldone effect · intentstate verifiedProduction AI Diagnostic
A bounded engagement for teams whose AI already touches real systems. We map one consequential workflow, expose its control gaps, and leave a clear path to hardening and verification.
Define the consequential workflow, actors, permissions, state changes, and business invariants.
Expose failure modes and engineer the controls that make the workflow safe to operate.
Prove the resulting business state, document the controls, and hand off operating evidence.
Proof
We prefer engineering evidence to decorative claims. Proof can be client work when permitted, or public labs, instruments, and technical artifacts when confidentiality limits what can be shown.
Whitestone lab · Public and reproducible
A public harness where every local control holds and the business state still ends wrong, invisible to task success, tracing, and reconciliation.
Architecture
Production patterns for authority, side effects, recovery, and verification.
See the patternsUse cases
Five agents and how we would engineer each, backed by the lab's failure catalogue.
See the use casesNot all evidence can be public. When client confidentiality limits disclosure, Whitestone shows the strongest evidence that can be shared truthfully.
Technology partnerships
Selected program memberships strengthen enablement and delivery. Each relationship is stated at its current evidence boundary—never as customer endorsement.
Partner Network · Member
Production OpenAI SystemsModel and agent architecture, evaluation, tool integration, deployment, and production controls.
Discuss this platformPartner Network · Registered Partner
Production AI on SnowflakeData foundations, AI and agent workflows, evaluation, reliability, and production operations on Snowflake.
Discuss this platformPartner Program · Member
Production AI on DatabricksData and AI architecture, RAG and agent systems, evaluation, governance, deployment, and operations.
Discuss this platformPartner Network · Member
Accelerated Production AIInference architecture, deployment, observability, and performance engineering on NVIDIA infrastructure.
Discuss this platformPartner · Member
Production AI on Google CloudModel deployment, data and agent architecture, evaluation, and production operations on Google Cloud.
Discuss this platformBuilt for
We partner with teams that ship and operate real systems—from strategy tied to architecture, through implementation and deployment, to durable production operations.
Real systems. Higher standards.
