Harness large language models, computer vision, and machine learning to build intelligent features that transform your product.
Every company wants AI features. Few know how to build them reliably. The gap between a ChatGPT demo and a production system that handles real users, real data, and real edge cases is enormous.
Hallucinations, prompt injection, cost explosions, latency issues, data privacy concerns — the failure modes of AI applications are unique and unforgiving.
You need a team that understands both the AI frontier and the unglamorous engineering required to ship it reliably at scale.
Not every problem needs AI. We evaluate whether AI is the right solution, which approach fits best, and what the realistic accuracy and cost targets are.
RAG pipelines, vector databases, embedding strategies, and data preprocessing — the foundation that determines whether your AI feature works or hallucinates.
Systematic prompt optimization, evaluation harnesses, and when needed, fine-tuning on your domain data for accuracy that generic models can't match.
Output validation, content filtering, cost controls, rate limiting, and monitoring. Your AI behaves predictably and stays within budget.
Model versioning, A/B testing, latency optimization, and continuous evaluation against ground truth datasets.
AI features that work consistently, handle edge cases, and degrade gracefully when they hit their limits.
Optimized inference pipelines, streaming responses, and intelligent caching for real-time user experiences.
Smart model routing, token optimization, and caching strategies that keep your AI bill predictable.
Feedback loops and evaluation frameworks that let your AI get smarter over time with real usage data.
PII handling, data residency, and model isolation designed for GDPR, HIPAA, and SOC2 requirements.
Abstraction layers that let you swap between OpenAI, Claude, open-source, or self-hosted models without rewriting code.
Detailed design document with model selection rationale, accuracy targets, and cost projections.
Fully deployed inference pipeline with caching, rate limiting, and error handling.
Vector database, embedding pipeline, and retrieval system optimized for your data.
Automated accuracy testing against ground truth datasets with regression alerts.
Real-time tracking of model usage, latency, accuracy, and cost per request.