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Decisions + controlB2B SOFTWARE / ENGINEERINGProject 09

AI Engineering Context & Review Assistant

A daily engineering assistant for troubleshooting, architecture readiness, data-model review, and evidence-linked standards guidance.

Target10-20%Escaped defect reductionCompare defects discovered after release across assisted and baseline review cohorts.
Target15-25%Review rework reductionMeasure repeated review cycles caused by missing standards, evidence, or context.
Modeled10-18%Incident recurrence reductionModel avoided repeat incidents from surfaced fixes, decisions, and runbook updates.

The business problem

As the engineering organization and toolset expanded, technical context became harder to locate. Debugging and design review required searching code, tickets, documents, prior decisions, and standards separately.

The same fragmentation affected architecture-change reviews and data-model quality checks. Reviewers repeatedly reconstructed organizational expectations instead of focusing on the engineering decision.

  • Software Engineering
  • Architecture
  • Data Engineering
  • Platform
  • Developer Experience

How the work changes

Today

  1. 01Describe the issueAn engineer starts with an error, document, design, or data model.
  2. 02Rebuild contextSearch repositories, tickets, runbooks, decisions, and standards.
  3. 03Review manuallyCompare the artifact with expected practices and prior designs.
  4. 04Act and documentResolve the issue or return feedback with supporting context.

With the system

  1. 01InspectThe assistant reads the technical question or submitted artifact.
  2. 02ContextualizeRelevant code, incidents, decisions, and standards are retrieved.
  3. 03EvaluateEvidence is compared with review criteria and known patterns.
  4. 04RecommendThe engineer receives grounded findings and decides what to change.

What the system does

An engineering assistant retrieves organizational evidence and evaluates artifacts against approved practices.

  • Retrieve relevant code, incidents, technical decisions, runbooks, and standards.
  • Support debugging with evidence from the organization's actual environment.
  • Evaluate architecture documents and data models against approved review criteria.
  • Return source-linked guidance while engineers retain responsibility for every change.

What this depends on

Technical sources are indexed with repository and document permissions intact.

Review criteria and engineering standards have accountable owners.

Generated findings are advisory and require engineer validation before action.

Start with clarity

If the business case isn’t there, we’ll tell you before you build.

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