AI Product Engineering
Design and ship AI-first products using LLMs, computer vision, and predictive analytics—productionized with secure, observable, and scalable pipelines.
Why AI-First Now
AI is now the core of modern software. LLMs, vector search, and CV unlock entirely new experiences—automated decisions, intelligent search, and copilots that learn across your stack.
We turn prototypes into reliable, compliant, and scalable AI products built for production from day one.
Our Engineering Approach
We start with clear measurable outcomes, model-selection scorecards, and reference architectures for latency, cost, and privacy.
Pipelines include data contracts, feature stores, experiment tracking, and CI/CD for models with human-in-the-loop review.
Security & Governance
Guardrails for prompt injection, Personally Identifiable Information (PII) redaction, rate-limiting, and audit trails are standard.
We align with SOC2 controls and adopt RBAC, secrets management, and observability across model lifecycle.
AI Architecture Blueprint
We design reference architectures covering retrieval-augmented generation (RAG), embeddings stores, feature stores, and real‑time inference layers.
Clear boundaries between orchestration, business logic, and model adapters enable safe iteration and model swaps without regressions.
Data Readiness & Labeling
High‑quality data wins. We help you instrument product flows, define data contracts, and stand up labeling pipelines with quality control.
Synthetic data generation, augmentation, and deduplication reduce bias and improve generalization across edge cases.
Evaluation & Guardrails
We create golden datasets and automated evals that track accuracy, toxicity, cost, latency, and hallucinations across releases.
Safety layers—content filters, PII scrubbing, prompt templates, and output validation—protect users and your brand.
MLOps & Observability
Versioning, experiment tracking, canary rollouts, and shadow traffic ensure reliable deployments with fast rollback paths.
Tracing, metrics, and logs provide visibility from prompt to vector lookup to model response and post‑processing.
Cost & Latency Optimization
We right‑size models, cache at multiple layers, batch requests, and use distillation/quantization where appropriate.
Autoscaling and adaptive routing select the cheapest model that meets your quality bar per request.
Compliance & Responsible AI
We document data provenance, provide model cards, and support DSAR/RTBF workflows for privacy regulations.
Human‑in‑the‑loop review ensures sensitive decisions are supervised and auditable.
Key Benefits
- Accelerate AI feature delivery with robust MLOps
- Reduce risk with governance, monitoring, and A/B evaluation
- Ship responsible AI aligned to compliance and security
Use Cases
- Conversational copilots for CRM and support
- Vision-based quality inspection and search
- Forecasting for pricing, inventory, and demand
Results
We shipped a domain-tuned copilot with evals, guardrails, and analytics. Productivity rose, and risk remained controlled via human-in-the-loop.
- Average Handle Time: -29%
- CSAT: +0.5
- Containment Rate: +23%
Frequently Asked Questions
How do you measure AI quality?
We define offline and online metrics—accuracy, latency, cost-per-call, hallucination rate—plus A/B tests and human review loops.
Which stacks do you support?
OpenAI, Anthropic, Vertex, Bedrock, LangChain/LangGraph, Triton/TensorRT, ONNX, Ray, and Kubernetes-based MLOps.
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