Custom LLM Systems
Assistants and agent workflows trained on your services, SOPs, calls, estimates and knowledge base.
AI Development & Architecture
Custom AI Architecture & Intelligent Systems.
We engineer bespoke AI and machine learning solutions for organizations that need more than prompt templates: custom LLMs, agent workflows, automation and deployment-ready infrastructure.
From concept to deployment
Enterprise AI development, engineered around your operation.
Building intelligent systems means turning operational problems into engineered solutions: intake agents, dispatch intelligence, sales copilots, reporting engines, knowledge retrieval, lead scoring, internal automations and feedback loops that learn from real outcomes.
We develop, integrate and tune AI around your actual data, workflows and business constraints — and we ship it on production-grade infrastructure that your team can run, monitor and extend.
What we engineer
Six categories of bespoke AI work.
Automation Engineering
AI workflows for routing, classification, drafting, summarization, lead handling, reporting and follow-up.
Model Training & Tuning
Dataset design, fine-tuning, instruction tuning, evaluation harnesses and continuous performance work.
ML / RL Infrastructure
Feedback loops that learn from operator corrections, job outcomes, conversion signals and customer behavior.
Full-Stack Integration
Connect AI to CRMs, websites, forms, call data, knowledge bases, dashboards and Cloudflare-native infrastructure.
Governance & Reliability
Guardrails, human-in-the-loop review, logging, permissions, evals and monitoring so AI can run safely in production.
Process
How we develop intelligent systems.
01
Discover
Map the workflow, the data sources, the cost of being wrong and the success metric the business actually measures.
02
Architect
Design models, retrieval, agents, tools, evaluation harness and infrastructure aligned to that metric.
03
Engineer
Build, train, fine-tune, integrate and ship to production with observability and rollback baked in.
04
Operate
Monitor evals, drift, cost and outcomes — feed real-world signal back into the next training cycle.
Deliverables
What you get.
Solution architecture
Dataset & eval harness
Fine-tuned models
Agent & RAG workflows
API & CRM integrations
Cloudflare Workers deploy
Observability dashboard
Guardrails & permissions
Runbooks & handoff docs
Continuous-improvement plan
FAQ
Common questions.
Do you build on top of OpenAI, Anthropic or open-source models?
All three. We choose based on the task, latency budget, cost profile and data-residency requirements. Many systems combine frontier and open-weight models behind a routing layer.
Where does the system run?
Most production deployments run on Cloudflare Workers, Durable Objects, Vectorize and Workers AI. We can also deploy to your existing cloud or hybrid setup when required.
How do you handle data privacy?
Data flow, retention and inference path are explicit deliverables in the architecture document. We support BYO keys, isolated tenants and on-prem inference where the use case demands it.
What does a typical engagement look like?
A 2 to 4 week discovery and architecture phase, followed by an 8 to 16 week build, and an ongoing operate phase if you choose. Scope and timeline are confirmed before any build work begins.
Develop AI that fits the business.
Bring us the workflow, bottleneck or growth constraint. We will turn it into an AI infrastructure plan.