AI & Automation Engineering

We build LLM-powered features that are reliable enough for production use — not chatbots that hallucinate or give contradictory answers. Our approach starts with prompt engineering that is tested against evaluation datasets with hundreds of edge cases. We implement retrieval-augmented generation (RAG) pipelines that ground LLM responses in your actual data, reducing hallucination rates dramatically. Output parsing enforces structured responses so downstream systems can consume AI output programmatically. Conversation memory is managed efficiently to control costs without losing context. We build moderation layers that catch harmful or off-topic responses before they reach users, and escalation paths that route complex queries to human agents seamlessly.

We automate the operational workflows that consume your team's time — document processing, email triage, data entry, report generation, approval chains, and customer onboarding sequences. Unlike no-code automation tools that break silently when an API changes, our automations are built in TypeScript with proper error handling, retry logic, and observability. Each workflow is modular and testable, with clear logging that shows exactly what happened and why. We connect your existing tools — Slack, email, CRM, databases, and custom APIs — through event-driven pipelines that trigger reliably and handle edge cases gracefully. AI agents are built with human-in-the-loop checkpoints for high-stakes decisions.

When off-the-shelf models are not enough, we build custom machine learning solutions tailored to your data and business logic. We handle the full pipeline: data cleaning and enrichment, feature engineering, model selection and training, hyperparameter optimization, and deployment behind production APIs. Common applications include demand forecasting, churn prediction, pricing optimization, anomaly detection, and recommendation engines. Every model ships with monitoring for data drift and performance degradation, automatic retraining triggers, and A/B testing infrastructure so you can measure real-world impact before full rollout. We document model behavior, limitations, and failure modes so your team can make informed decisions about when to trust the output.

We build intelligent document processing pipelines that extract structured data from invoices, contracts, medical records, and other unstructured sources. Our approach combines OCR, layout analysis, and LLM-based extraction to handle the variability that rules-based systems cannot. We implement confidence scoring and human review queues for documents that fall below accuracy thresholds. Output is validated against schemas and delivered to your systems via APIs or database writes. Processing scales horizontally to handle thousands of documents per hour, with cost optimization that routes simple documents to cheaper models and reserves expensive processing for complex cases.

AI features that are not cost-controlled become budget nightmares at scale. We build inference infrastructure with semantic caching that serves identical or similar queries from cache, reducing LLM API costs by 60-80%. Smart routing directs simple queries to smaller, cheaper models and reserves expensive models for complex tasks. Rate limiting and token budgets prevent runaway costs from unexpected usage spikes. We implement structured logging for every AI interaction so you can analyze usage patterns, identify optimization opportunities, and demonstrate ROI to stakeholders. Monitoring dashboards show latency, cost per query, accuracy metrics, and user satisfaction in real time.