AI engineering & consulting
We design, build and optimize production-ready AI agents, document intelligence systems and custom LLM/SLM solutions for real-world business workflows.
How we engineer AI for production
Clients
We work with startups, technology companies and enterprises looking to build, scale or improve real-world AI systems.
Companies building AI-powered products that need specialized AI engineering expertise.
Organizations with existing technology teams that need additional expertise in AI agents, document intelligence, LLM/SLM engineering, optimization or AI security.
Companies looking to identify and implement practical, high-value AI use cases across their business processes.
What we do
We build AI systems for production, taking responsibility for the engineering decisions that determine whether they actually work in the real world.
Services
Each pillar is a discipline we own end to end, engaged individually or as a full build.
Build production-ready AI agents that reason, use tools, and automate complex business workflows.
Turn complex documents into structured, actionable data with AI-powered extraction and reasoning.
Build models that perform better on the tasks and domains that matter to your business.
Reduce AI latency and inference costs while scaling production workloads.
Build AI systems you can measure, monitor, and trust in production.
AI engineering approach
Every AI system we build is designed around measurable quality, controlled risk, predictable cost and long-term maintainability.
Every system starts with task-level evaluation. We measure quality against defined targets before optimizing models, prompts or workflows.
We do not default to the biggest model. We find the best balance of quality, latency and cost for the task.
Real AI systems fail. We design for it with fallbacks, retries, confidence thresholds and human review where needed.
Security starts in the architecture. Tool permissions, guardrails, prompt-injection defenses and PII protection are built in from the beginning.
Cost per task, latency and throughput are treated as engineering metrics from the first prototype, not after deployment.
We leave your team with production-ready code, documented architecture and the knowledge to operate and evolve the system independently.
Case studies / results
Anonymized summaries of the kind of work we deliver. Full references available on request under NDA.
Invoice and claims intake
A document pipeline with classification, extraction and validation, routing only low-confidence cases to reviewers. Manual handling drops to exceptions.
LLM cost optimization
Model routing, caching and a fine-tuned small model for the highest-volume step, benchmarked against the original quality bar.
Inference optimization
Quantization, batching and serving changes applied against production traffic profiles to cut tail latency.
Agent red teaming
Adversarial testing of tool use and prompt injection paths, followed by guardrails and monitoring, so an agent could go live.
AI advisory
Not sure how to approach an AI project? Start with a single conversation. We review your idea or existing system and give you a straight technical read on feasibility, architecture, risk and cost.
Next step
Bring the workflow, the constraints and the data. We will tell you what is realistic, what it costs and how we would build it.
