AI Development
Production AI, not conference-demo AI.
Cyfox designs and ships production AI systems for US and Canadian companies: autonomous agents, LLM-powered features, retrieval-augmented generation (RAG) pipelines, and conversational interfaces. Engagements run as outsourced delivery — a senior AI engineering team handles architecture, evaluation, guardrails, and deployment, priced well below in-house AI hires.
[ 01 / capabilities ]
What this service covers.
01
AI agents & workflow automation
Agents that execute multi-step business workflows — triage, research, document processing — with human-in-the-loop checkpoints and full audit trails.
02
LLM integration & RAG
LLM features inside your existing product: retrieval pipelines over your data, structured extraction, summarization, and classification with measured accuracy.
03
Conversational AI
Customer-facing chat and voice assistants grounded in your knowledge base, with escalation paths and tone control.
04
Evaluation & guardrails
Eval suites, regression testing for prompts, cost and latency budgets, and safety guardrails — the unglamorous work that separates production AI from prototypes.
[ 02 / the details ]
How we run it.
Why outsource AI development
Senior AI engineers are the scarcest hires in North America — compensation for experienced LLM engineers regularly exceeds $250,000 before equity, and recruiting cycles run three to six months. Outsourcing the build gets a working system into production in weeks while your own hiring catches up, and the architecture, evals, and documentation transfer to whoever runs it long-term.
Our delivery approach
AI projects fail when accuracy is asserted instead of measured. Every Cyfox AI engagement defines an evaluation set before the first prompt is written: real inputs, expected outputs, and a target score. We iterate against that benchmark, report it weekly, and ship behind feature flags so rollout risk stays controlled. Model choice is pragmatic — Claude, GPT, Gemini, or open-weight models — selected per task on measured quality, latency, and cost rather than brand loyalty.
Data security for AI systems
Client data never trains third-party models. We default to zero-retention API configurations, keep retrieval indexes inside your cloud account, and document every data flow. For regulated industries we deploy within your VPC and follow HIPAA- and GDPR-aware handling end to end.
[ 03 / questions ]
AI Development — FAQ.
How much does it cost to build an AI agent or LLM feature?
A scoped LLM feature (e.g., RAG-powered search or document extraction) typically runs $25,000–$70,000 to production. Autonomous agent systems with evaluation suites and guardrails generally range $60,000–$150,000 depending on workflow complexity.
Which LLM do you build on — Claude, GPT, or open-source models?
We select per task based on measured evaluation scores, latency, and cost. Most production systems we ship use a mix: a frontier model for complex reasoning and a smaller, cheaper model for high-volume steps.
Will our data be used to train AI models?
No. We use zero-data-retention API configurations by default, keep embeddings and indexes in infrastructure you control, and put those guarantees in the contract.
How do you measure whether the AI actually works?
Every engagement starts with an evaluation set built from your real data — inputs, expected outputs, and a target accuracy score. We report against it weekly, and "done" is defined by the benchmark, not by a demo.
Can you add AI features to our existing product?
Yes — most of our AI work is integration into existing codebases. Your team keeps shipping; ours delivers the AI feature behind a flag with evals, docs, and a handover.
[ next step ]
Need ai development? Tell us the spec.
NDA first. A scoped estimate inside one week. No pressure after.