AI workflow production readiness

CoreInnovation

Turn a promising AI workflow into a dependable production system.

Senior engineering for evaluation, validation, human review, reliability, and operations.

Request a fit call Explore the sprint
Production path / One workflowEngineering controls around the model
4 controls applied
Current stateWorking demoProduction confidence / unresolved
  1. 01DefineOutcome
  2. 02EvaluateEvidence
  3. 03ControlReview
  4. 04SustainOperations
Target state Resolved
Dependable system
  • Measurable
  • Reviewable
  • Operable
See if your workflow is ready

Readiness diagnostic

The demo is working. The system is not dependable yet.

Production readiness starts by making uncertainty visible. These signals show where a promising workflow needs stronger engineering controls.

  1. 01

    Reliability

    The prototype works on demos but fails unpredictably.

  2. 02

    Evaluation

    The team cannot tell whether model changes improve the workflow.

  3. 03

    Output contract

    Outputs need strict schemas, traceability, or human approval.

  4. 04

    Operations

    Retries, cost, state, or observability are becoming operational problems.

  5. 05

    Delivery path

    The team needs a production plan before a larger build.

Workflow readiness
0 / 5 assessed
01ReliabilityUnstable
02EvaluationNo baseline
03Output contractUnbounded
04OperationsDegraded
05Delivery pathUnscoped
Readiness result
Readiness gaps mappedA defensible production path
See the production-readiness sprint

Flagship engagement

AI Workflow Production Readiness Sprint

Turn one uncertain AI workflow into an evidence-backed production plan.

Starting at $3,0007-10 business days
Discuss this sprint

What we examine

  • Map stages, data boundaries, and failure modes
  • Establish representative evaluation cases
  • Review validation, observability, security, latency, and cost
  • Identify the smallest defensible production path

What you leave with

  • Current-state architecture and risk map
  • Evaluation baseline or evaluation design
  • Validation and failure-handling recommendations
  • Prioritized implementation plan and findings walkthrough
Production control mapOutcome protected
EvaluationMeasurable
ValidationBounded
ObservabilityVisible
Human reviewExplicit

Evidence

Production experience without client-specific details.

Client-specific details stay private. The engineering concerns shown here are the ones that determine whether an AI workflow can be trusted in production.

Production AI workflow

From complex source material to structured inputs with expert review.

Designed and built a production multi-stage AI workflow that organized high-trust source material into structured inputs, moving experts from research-intensive work to focused review while preserving traceability and final human decisions.

  • Evaluation harnesses and representative expert-reviewed cases
  • Strict schema validation that rejects malformed model output
  • Durable retries, state management, and document storage
  • Real-time progress and source traceability for human review

Generalized production pattern

Engineering controls around the model.

Production AI workflowObservable · bounded · reviewable
Multi-stage execution
Evaluation harness
Structured validation
Traceable review

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Other ways to work

Start at the right depth.

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Working process

Keep the outcome clear as the system gets more capable.

01

Diagnose

Define the workflow, desired outcome, data boundaries, and current failures.

02

De-risk

Establish evaluation, validation, approval, and architecture boundaries.

03

Deliver

Provide the agreed plan, implementation, verification, and team handoff.

Brian Dunams, founder of Core Innovation

Founder-led

The engineer in the conversation does the work.

Based in Atlanta, Core Innovation is led by Brian Dunams, whose experience spans healthcare, platform systems, full-stack products, and production AI workflows. Engagements stay direct from diagnosis through handoff.

The person shaping the recommendation remains accountable for how it works in production.
About Core Innovation View Brian's portfolio

READINESS PATH OPEN · ONE WORKFLOW · CLEAR NEXT STEP

Have one AI workflow that needs to become dependable?

Request a fit call