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Capabilities

AI-first systems engineering, end-to-end

Shipping AI is easy to demo and notoriously hard to keep reliable. We build production-grade AI and software systems with measurable evaluation, security by default, scalable infrastructure, and long-term maintainability.

RAG + SearchAgents + ToolsEval HarnessesSafety + GuardrailsGCP + CI/CDObservabilityData PipelinesUX for AI

A wide range of capabilities, one delivery standard

You don’t need “AI consultants.” You need builders who can ship, measure, secure, and operate the system.

AI Products & Agents

Assistants, copilots, workflow agents that actually behave in production.

  • Agentic workflows with tool use, retries, guardrails, and deterministic fallbacks
  • RAG that survives real data, real users, and real failure modes
  • Eval harnesses, regression suites, and monitoring so quality doesn’t drift quietly
  • Human-in-the-loop patterns for approvals, escalations, and auditability
AgentsRAGEvalsMonitoringGuardrails

Outcome: AI features that stay trustworthy under real user pressure.

Applied AI Research

Fast iteration, but with rigor: baselines, ablations, benchmarks, and decisions you can defend.

  • Experiment design, baselines, ablations, and metric selection
  • Retrieval quality work: chunking, reranking, hybrids, query rewriting
  • Safety and robustness testing (prompt injection, data leaks, jailbreak attempts)
  • Turning results into an implementable plan, not a theoretical PDF
BenchmarksAblationsSafetyReproducibility

Outcome: Validated direction with measurable gains and clear tradeoffs.

Data & Knowledge Systems

Clean models, indexing, pipelines, governance. The unsexy foundation that makes AI work.

  • Schema + lineage, data contracts, and audit-friendly flows
  • Search and retrieval pipelines (vector + keyword + hybrid)
  • ETL/ELT, backfills, incremental loads, and change data capture patterns
  • Quality checks, dedupe, PII handling, retention, and access control
PostgresVector SearchETLGovernanceQuality

Outcome: Data that’s queryable, trusted, and engineered for scale.

Cloud, MLOps & Reliability

Deployments that don’t melt at the first traffic spike or model update.

  • GCP deployments, CI/CD, environments, secrets, and release safety
  • Observability: logs, traces, metrics, SLOs, and alerting that matters
  • Model lifecycle support: versioning, rollbacks, drift checks, canaries
  • Cost-aware architecture and performance tuning for inference + pipelines
GCPCloud RunCI/CDSLOsDrift

Outcome: Predictable operations, clear telemetry, controlled change.

Product Interfaces

UI that makes complex systems feel obvious. Adoption is a feature.

  • Web platforms, dashboards, internal tools, and client-facing experiences
  • Design systems + component libraries for consistency at speed
  • Accessibility, performance, and the boring details users notice instantly
  • AI UX patterns: citations, confidence cues, review flows, explainability UI
Next.jsDesign SystemsAccessibilityPerformance

Outcome: Premium UX that supports adoption, not friction.

Developer Tools

Internal platforms that cut cycle time and reduce recurring mistakes.

  • CLI tools, internal portals, and automation that teams actually use
  • Workflow tooling: replay, inspection, sandboxing, and safe experimentation
  • Golden-path templates for services, observability, and deployment
  • Guarded integrations to prevent “oops we shipped a secret” moments
CLIAutomationInternal PlatformsTooling

Outcome: Faster iteration and fewer operational faceplants.

Security, Privacy & Safety

Security-by-default, not security-by-apology.

  • Threat modeling for AI systems (prompt injection, data exfiltration, tool abuse)
  • Secrets handling, least privilege, and secure-by-default service patterns
  • PII strategy: redaction, retention, access control, and audit logs
  • Policy + enforcement: allowlists, sandboxing, and safe tool boundaries
Threat ModelPIIAccess ControlAuditSandboxing

Outcome: Safer systems with fewer surprises and cleaner compliance.

Data Science & Analytics

Decision-grade analytics, not dashboard theater.

  • Metric definitions, event models, and instrumentation strategy
  • Experimentation and evaluation: what moved, why, and by how much
  • Funnel + retention analysis, cohorting, and anomaly detection
  • Performance reporting that aligns engineering with business reality
MetricsExperimentationInstrumentationAnalysis

Outcome: Clear signals that guide product and engineering decisions.

Outcome-driven delivery (not vague capability theater)

We make scope explicit, measure impact, and leave behind artifacts your team can run without us.

What you get (the concrete stuff)

  • A scoped system plan with success metrics and acceptance criteria
  • Production implementation with tests, runbooks, and release strategy
  • Evaluation harness + regression suite for quality over time
  • Observability: logs/metrics/traces + alerts tied to user outcomes
  • Security posture: threat model, mitigations, and safe defaults

Common builds

  • Enterprise RAG search and knowledge assistants
  • Support + operations copilots with tool access and approvals
  • Automated doc processing pipelines (extract, normalize, index, verify)
  • Model routing, caching, and cost controls for inference
  • Internal platforms that standardize deployment and reliability

Hard problems we like (because we’re broken)

  • Retrieval quality: chunking + reranking + hybrid search + eval design
  • Hallucination control via grounding, constraints, and fallback logic
  • Prompt injection defense and tool boundary hardening
  • Scaling systems: perf + cost optimization + safe rollout patterns
  • Data governance and auditability for regulated environments

Proof you can ask for

If a vendor can’t show these, you’re buying vibes. Vibes do not pass audits.

  • Eval report with baselines + failure analysis
  • Acceptance criteria + test plan mapped to metrics
  • Runbooks, rollback plan, and on-call readiness notes
  • Threat model for AI-specific risks and mitigations

How we deliver reliably

Problem framing → architecture → implementation → evaluation → deployment → observability → maintenance.

01
Frame the problem
Define success metrics, constraints, and the real business workflow (not the slide-deck version).
02
Architect + de-risk
Prototype the risky bits early: retrieval, tool use, safety, latency, and cost ceilings.
03
Build production
Implement with disciplined engineering: tests, reviews, CI/CD, and minimal operational overhead.
04
Measure and improve
Evals, regressions, monitoring, and targeted iteration so quality doesn’t decay after launch.
05
Own go-live
Runbooks, rollback plans, on-call readiness, and clear ownership post-deploy.

Engagement models

Choose the fastest path to value. We’ll still hold the line on engineering quality.

Rapid Discovery Sprint

1–2 weeks
  • System plan + architecture
  • Risk spikes + proof points
  • Metric definition + eval plan
  • Implementation roadmap

Build & Ship

4–10+ weeks
  • End-to-end implementation
  • Deployment + observability
  • Evals + regression suite
  • Security hardening + docs

Retained Reliability

Monthly
  • Monitoring + drift checks
  • Iteration on quality + cost
  • New features safely shipped
  • Operational support & tuning

If you want it to work in production, not just a demo

Send your problem statement, constraints, and any existing architecture. We’ll respond with a plan and next steps.