Time-boxed pushes to hit KPIs, with reversible rollouts and measurable acceptance criteria.
AI Architecture Audit
A fixed-scope audit that measures where your LLM stack leaks money and accuracy, run on your real usage data, ending in a prioritized fix plan your team can execute.
•Measured leakage, not estimates
•Prioritized fix plan with effort sizing
•Free 3-minute diagnostic to start
What you get: Measured cost-leakage analysis from your real usage exports (billing, token logs), not modeled assumptions · Retrieval and chunking review: over-retrieval, K and hop settings, context-window load, embedding fit · Model-routing review: which calls belong on cheaper models, caching opportunities, batch candidates · Failure-mode assessment: grounding risk, drift exposure, prompt-injection surface for agent tooling · Prioritized fix plan with cost and effort sizing for each finding · Executive readout: a written brief your leadership can act on without us in the room
Agentic AI Systems
Multi-agent systems that carry out back-office decisions under policy guardrails, with a human reviewer on the exceptions and an audit trail on every action.
•Eliminate manual back-office steps
•Policy-aligned actions
•Explainable decisions
What you get: Orchestration graph in code (agents, tools, policies) with retries/timeouts · Evals suite: jailbreak, toxicity, groundedness, accuracy gates · Guardrails and safety gates enforced in CI and runtime · Reviewer console (approve/annotate/retry) with audit trail · Run log and trace viewer (inputs, prompts/versions, tool calls, outputs) · Budget caps and alerts; cost per transaction export
GenAI Product Accelerator
A build track that ships a production RAG feature grounded in your own data, with accuracy evals, safety gates, and cost-per-query tracking.
•MVP in weeks
•Measurable accuracy
•Usage analytics & observability
What you get: Vector pipeline + knowledge ingestion (automated re-indexing) · RAG orchestration layer with prompt versioning and fallbacks · Evals suite: accuracy (exact-match + semantic), hallucination gates, toxicity filters · CI/CD integration with regression gates (accuracy thresholds) · Observability dashboard: usage, cost per query, latency p95 · Safety monitoring: PII detection, content filters, rate limits
Computer Vision FastTrack
Takes a computer-vision model from proof-of-concept to production on edge or cloud, with measured precision, low-latency inference, and drift monitoring.
•Detect/track reliably
•Low latency at the edge
•Ops dashboards that stick
What you get: Custom model trained on your footage (detection/tracking/classification) · Edge deployment package: ONNX/TensorRT optimized for Jetson/x86/ARM · Inference pipeline with <target latency (typically 60-200ms p95) · Precision/recall benchmarks on test set with confusion matrices · MLOps workflow: drift monitoring, review UI, re-labeling, retraining hooks · Production runbook: deployment, rollback, troubleshooting, scaling
Data & Analytics Platform
KPI and GIS dashboards on a governed warehouse, with data-quality checks and alerting so the numbers your team acts on hold up.
•Faster ops insight
•DQ pipeline with alerts
•Narrative reporting with anomalies
What you get: Data connectors with retry logic and monitoring (source → warehouse) · KPI catalog with definitions, owners, refresh schedules, and SLAs · Data quality pipeline: profiling, validation rules, alerts on critical failures · GIS-enabled dashboards with zoom, filter, layer controls, and export · Narrative report generator with automated summaries and anomaly detection · Runbook: troubleshooting, scaling, adding KPIs, data refresh procedures
MLOps & Model Operations
Model registry, drift monitoring, and one-click rollback that keep already-deployed models reliable months after launch, not just on launch day.
•Traceable models
•Drift alerts
•Fast rollback
What you get: Model registry with versioning, lineage tracking, and metadata tagging (MLflow/W&B/custom) · Drift monitoring dashboard with statistical tests (KL divergence, PSI, data quality) · Automated rollback workflow with last-known-good fallback and rollback criteria · Evaluation harness with precision/recall/F1 benchmarks and confusion matrices · CI/CD integration for model deployment with GitHub Actions/GitLab CI pipelines · MLOps runbook with troubleshooting, scaling guidelines, and cost guardrails
Platform Modernization
Modernizes a legacy platform without a feature freeze: modular boundaries first, then observability and cost controls, with dual-run validation for a zero-downtime cutover.
•No feature freeze
•p95 down, cost down
•Safer releases
What you get: Modernization blueprint with target architecture and migration phases · Dual-run infrastructure with blue-green/canary deployment capability · Observability stack: distributed tracing, structured logging, error budgets · API gateway and module boundaries with contracts and versioning · CI/CD pipeline with automated testing and rollback automation · Security baseline: SBOM generation, SAST/DAST scanning, CSP/HSTS headers, secret rotation via KMS · Cost plan: unit economics per request/service, autoscale policies, budget alerts and rightsizing recommendations · Performance report: p95 latency, throughput, cost analysis, error rates
AI Security & Compliance
Security hardening built for AI failure modes (prompt injection, model theft, data poisoning), mapped to SOC 2, NIST AI RMF, HIPAA, and CJIS with audit-ready evidence.
•AI threat protection
•Continuous monitoring
•Audit-ready evidence
What you get: AI threat assessment: prompt injection, model theft, data poisoning vulnerability analysis · LLM security hardening: input validation, output filtering, jailbreak protection · Model access controls: RBAC, API key management, usage monitoring · AI-specific SBOM: model lineage, training data provenance, dependency tracking · Compliance mapping: SOC 2, ISO 27001, HIPAA, NIST AI RMF alignment · Continuous monitoring: anomaly detection, drift monitoring, incident response · Audit evidence pack: security controls documentation, penetration test results, compliance artifacts
Product Pods
A dedicated cross-functional pod that owns a slice of your roadmap and ships on a predictable cadence, with transparent velocity and quality metrics.
•Velocity without churn
•Transparent cadence
•Lower risk
What you get: Pod charter with clear ownership, scope boundaries, and escalation paths · Weekly sprint demos with stakeholder Q&A and feedback loops · Bi-weekly retrospectives with actionable improvement items tracked · Monthly roadmap health review: velocity trends, at-risk epics, dependency status · Real-time backlog visibility: definition of ready/done, acceptance criteria templates · Incident response SLOs: P0/P1 triage, post-mortem reports, mitigation tracking · Transparent metrics dashboard: sprint completion, carry-over %, bug density, PR cycle time
AI Platform & Orchestration
One control plane to route, run, observe, and govern multi-model and multi-agent apps, with cost policies, guardrails, and audit trails.
•77% lower AI run costs
•99% faster model switching
•Full audit compliance
What you get: 3-5 week build · orchestrator + guardrails · run log + observability
Rapid Prototyping Lab
A two-week lab that turns a risky idea into a user-tested prototype and a technical spike, ending in a clear build, park, or pivot call.
•Stakeholder buy-in
•Validated UX + tech spike
•Clear build/no-build call
What you get: 2-week lab · user-tested prototype · tech spike
Integration FastTrack
Production-ready connectors between your systems, with contract tests, retry and idempotency logic, and dual-run reconciliation before cutover.
•Zero-surprise cutovers
•Fewer data errors
•Battle-tested retries
What you get: Spec pack: sequence diagrams, auth scopes, OpenAPI/Protobuf/Avro/JSON schema · Test harness: unit/contract/integration; replayable fixtures; mock servers · Error taxonomy + retry/backoff and idempotency keys · Reconciliation jobs + variance dashboards; backfill plan · Runbooks: deploy/rollback, replay, DLQ handling, on-call workflow · Security: SAST/DAST/SCA clean; secrets in KMS; PII masking; least-privilege · Docs: setup, versioning policy, change log, support SLAs
Rails Upgrades without Feature Freeze
In-place Rails and Ruby upgrades while you keep shipping, using dual-boot and blue-green cutovers, safe migrations, and measurable p95 and CVE wins.
•Same-day cutovers, no freeze
•p95 down ≥ 30%
•Zero critical CVEs
What you get: Upgrade plan: Ruby X→Y, Rails A→B; gem audit and shim strategy · Dual-boot enabled; green path proven in staging with traffic replay · Zero-downtime migrations via strong_migrations / gh-ost / pt-osc; backout path · CI/CD hardening: matrix builds (old/new), contract tests, flaky-test quarantine · Observability pack: pre/post p95, p99, error budgets, Slow Query log reports · Performance fixes: N+1 elimination; index strategy, partitioning; cache keys · Security hardening: Brakeman, bundler-audit/Snyk, CSP/HSTS, CSRF, session store, key rotation · Cost controls: puma worker math, pgbouncer, env-specific pool sizes, object store offload · Cutover runbook: canary %, health gates, 'abort switch,' rollback <10 min · Post-go-live hypercare (2-4 weeks) with SLO watch
DevOps & Platform Engineering
Your edge and cloud infrastructure as versioned code, with CI/CD that rolls back in minutes, tracing across edge and origin, and hard spend caps on platforms that ship without them.
•Everything as code
•Tested rollback
•Spend guardrails
What you get: Terraform-codified estate: Cloudflare zones, WAF, DNS, Access policies, and AWS resources imported from console click-ops into versioned modules with drift detection · CI/CD pipelines with progressive delivery: canary releases, percentage traffic splitting, automated rollback, and per-PR preview environments (GitHub Actions or GitLab CI) · Observability across edge and origin: OpenTelemetry tracing, dashboards, SLOs with error budgets, and alert runbooks wired to your on-call · Spend guardrails: rate limits, usage circuit-breakers, per-service budgets, and anomaly alerts on platforms that have no native billing kill-switch · Zero Trust access operations: SSO-backed policies for admin surfaces, internal apps, and CI pipelines, with quarterly access reviews · Failure-mode design: origin fallback, graceful degradation paths, and multi-CDN options for revenue-critical routes, each one tested, not assumed · Operations runbook and 30-day support, with an optional managed retainer for monitoring, rule lifecycle, and incident response
Estimate your payback in minutes.
Choose a scenario and get a defensible Year-1 ROI with exportable assumptions.
Estimates only; we validate assumptions during a 30-min consult.
