Selected solutions.

Representative solutions showing how we approach each problem class. Client engagements will be published here as they are cleared.

REPRESENTATIVE SOLUTION

AI SDLC automation

01 / 06

PROBLEM: Delivery slowed by manual handoffs between ticketing, code review, testing, and release.
ARCHITECTURE: An orchestrator coordinating planning, coding, review, and testing agents over GitHub and CI, with human approval at merge and release.
WHAT WE BUILT: An agent pipeline from ticket to pull request, automated test generation, policy checks, and generated release notes.
BUSINESS OUTCOME: Shorter path from requirement to production, with every agent action reviewable by the team.

REPRESENTATIVE SOLUTION

02 / 06

PROBLEM: Environment requests took weeks and drifted from security baselines over time.
ARCHITECTURE: A natural-language request layer feeding an infrastructure agent that generates Terraform, validated by policy-as-code before apply.
WHAT WE BUILT: Self-service provisioning, continuous drift detection, and remediation proposals routed for approval.
BUSINESS OUTCOME: Consistent, compliant environments provisioned on demand.

REPRESENTATIVE SOLUTION

03 / 06

PROBLEM: Teams spent hours searching across wikis, tickets, and documents for operational answers.
ARCHITECTURE: Permission-aware retrieval over company sources, grounded generation, and citations on every answer.
WHAT WE BUILT: An assistant in Slack and on the web that respects existing document permissions.
BUSINESS OUTCOME: Faster answers grounded in company sources, with access controls preserved.

REPRESENTATIVE SOLUTION

04 / 06

PROBLEM: Incident triage required senior engineers to correlate logs, deploys, and code changes by hand.
ARCHITECTURE: Specialized agents for logs, traces, deployments, and code, coordinated by a supervisor with shared memory.
WHAT WE BUILT: A triage workflow producing ranked hypotheses and suggested remediation for on-call review.
BUSINESS OUTCOME: Faster diagnosis, with engineers retaining the final decision.

REPRESENTATIVE SOLUTION

05 / 06

PROBLEM: Each team maintained its own deployment scripts and cluster configuration.
ARCHITECTURE: An internal developer platform with golden paths, GitOps delivery, and shared observability.
WHAT WE BUILT: Service templates, paved-road CI/CD, and a self-service portal for environments.
BUSINESS OUTCOME: Teams ship through one consistent, supported path to production.

REPRESENTATIVE SOLUTION

06 / 06

PROBLEM: LLM features in production lacked visibility into quality, latency, and cost.
ARCHITECTURE: Tracing for every model call, evaluation pipelines, and cost attribution by feature.
WHAT WE BUILT: Dashboards, regression evaluations in CI, and alerting on quality drift.
BUSINESS OUTCOME: AI behavior that can be measured, compared, and governed.

Tell us the workflow, platform, or operational challenge. An engineer will reply with questions.