AI ENGINEERING STUDIO // CLIENT SOLUTIONS

Solving complex problems.
Delivering production AI.

We partner with ambitious companies to solve difficult technical challenges—building custom AI systems, autonomous agents, and high-assurance web software that earn their place in production.

Technical Rigor

How We Architect Production AI Systems

Architecture Blueprint // Applied AI Pipelines

AI Systems with Strict Output Guardrails

We design LLM workflows that treat the model as a powerful but unreliable component, wrapping it in validation schemas, fallbacks, and agreed latency and cost budgets.

01 / Input DefenseScreen

Prompt injection screening & PII redaction

02 / Hybrid RetrievalGround

Dense embeddings + lexical re-ranking

03 / Constrained InferenceStructure

Schema-constrained output & typed tool calls

04 / Output GuardrailValidate

Schema validation & citation checks

What we measure and report
P95 latencySchema conformanceCost per request
Areas of Focus

Engineering Capabilities

Practical AI systems, rigorous evaluation, and production web engineering.

01 / APPLIED AI & AGENTS

Reliable AI Systems & Tool Calling

We build LLM workflows and agents that return validated, structured output. Schema enforcement, hybrid retrieval (dense + lexical) and fallback paths keep hallucinations and model failures contained, and visible when they happen.

Autonomous Tool CallingHybrid Vector RAGSchema-Constrained DecodingLatency & Cost Optimization
02 / PRODUCT & CLOUD DEV

Production-Ready Web & Cloud Engineering

From a first feasibility prototype to production software. We build fast, resilient Next.js and full-stack cloud systems with strict typing, sensible architecture, and code your team can maintain after we leave.

Next.js 16 (App Router)TypeScript Strict ModeEdge Proxying & CachingPostgreSQL / Distributed DBs
03 / EVALS & HARDENING

Evaluation Suites & Continuous QA

Automated evaluation pipelines that measure real-world performance against curated test sets, so regressions are caught before production and accuracy, safety and latency are tracked release over release.

Synthetic Fuzz TestingCalibrated LLM JudgesRegression HarnessesSecurity & Injection Shields
Collaboration Formats

How We Partner With You

Feasibility Sprint

2 to 3 weeks

Finding out whether an AI idea works on your real data before you commit a budget to it.

Deliverables:
  • ✓Working prototype on your data
  • ✓Accuracy, latency & cost report
  • ✓Clear go / no-go recommendation
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Ongoing Build Partnership

1 to 6 months

Shipping an AI feature or product end to end, working alongside your team in weekly increments.

Deliverables:
  • ✓Continuous production shipping
  • ✓Automated CI/CD & eval suites
  • ✓Full code ownership & docs
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Quality & Security Audit

1 to 2 weeks

Pre-launch review of architecture, security basics, and performance.

Deliverables:
  • ✓Security & architecture findings
  • ✓Before/after Core Web Vitals report
  • ✓Pull requests with fixes
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