AI Engineering
We design and build production-grade AI systems — from custom LLM applications and intelligent agents to ML pipelines that run reliably at scale.
What We Deliver
- Custom LLM application development with structured output guarantees
- Multi-agent orchestration systems with tool-use and memory
- Retrieval-augmented generation (RAG) pipelines with evaluation frameworks
- ML pipeline engineering, training infrastructure, and MLOps automation
- Model fine-tuning, distillation, and performance benchmarking
- Prompt engineering systems with version control and A/B testing
Typical Use Cases
Enterprise copilots
Internal knowledge assistants that understand your domain, integrate with existing tools, and produce reliable, cited outputs.
Document intelligence
Automated extraction, classification, and summarization of unstructured documents at scale — contracts, invoices, medical records, legal filings.
Agentic workflows
Autonomous agents that execute multi-step business processes — research, analysis, reporting — with human-in-the-loop checkpoints.
Real-time content generation
Production systems that generate personalized content, recommendations, or responses with sub-second latency and quality guardrails.
How We Work
Discovery & Architecture
We map your use case to the right AI architecture — evaluating model selection, data requirements, latency constraints, and cost trade-offs before writing code.
Prototype & Validate
A working prototype with real data in weeks, not months. We build evaluation harnesses early to measure what matters: accuracy, latency, cost per query.
Production Hardening
Structured error handling, fallback chains, observability, rate limiting, and deployment automation. Every system ships with monitoring built in.
Operate & Iterate
Continuous evaluation against drift, model updates, and changing requirements. We hand off systems that your team can maintain and evolve.
Technology Expertise
Frequently Asked Questions
How do you evaluate whether a use case is a good fit for LLMs?
We assess three dimensions: task structure (is the output well-defined enough to evaluate?), data availability (do you have representative examples?), and error tolerance (what happens when the model is wrong?). Not every problem needs an LLM — sometimes a well-tuned classifier or rule engine is the right answer.
What does production-grade mean for AI systems?
It means structured error handling, evaluation pipelines that run in CI, observability dashboards tracking accuracy and latency, cost controls, fallback chains for when models fail, and deployment automation. A demo that works 80% of the time is not production-grade.
How do you handle hallucination and output quality?
Through a combination of retrieval grounding, structured output schemas, automated evaluation suites, and human-in-the-loop review for high-stakes decisions. We build guardrails into the system architecture, not as an afterthought.
Can you work with our existing models and infrastructure?
Yes. We're model-agnostic and infrastructure-flexible. Whether you're running OpenAI APIs, self-hosted open-source models, or a hybrid setup, we architect systems that work with your constraints.
What's your approach to RAG systems?
We treat RAG as a retrieval engineering problem, not just a vector search problem. That means investing in chunking strategy, metadata enrichment, hybrid retrieval, re-ranking, and continuous evaluation — not just plugging documents into an embedding database.
Ready to build with us?
Tell us about your project and we'll scope a pragmatic path forward.