Discover & Scope
Align on the decision, the data reality, and the number that proves it worked. Opportunity brief, KPI model, phased roadmap, cost ranges.
AI Agents & LLMs,
Shipped
AI agents, LLMs, generative AI, fine-tuning, RAG and computer vision — engineered, evaluated
and deployed to production. 96 AI deployments. 38 countries served.
Artificial Intelligence in Production
Most AI programs stall between demo and production. Cryptonic closes that gap. We design, build and operate the whole stack — autonomous agents, LLM applications, retrieval-augmented generation, fine-tuned models and computer vision — with the same rigor we bring to fraud decisioning and clinical systems. Every engagement starts with three things: the decision the model has to make, the data available to make it, and the number that proves it worked. We build the evaluation harness before we build features, so accuracy, latency, safety and cost per call are measured from week one instead of discovered in production. Our engineers own the whole path — data pipelines, model selection, guardrails, observability and the rollback plan — and one senior lead stays accountable for the outcome. 96 AI deployments are live today, including sub-15ms fraud decisioning across 40M daily transactions for a top-10 retail bank.
The result is a system that holds under real load — with acceptance thresholds you signed off, evaluation dashboards you can read, and a senior engineer accountable for the outcome. Not a pilot: production software, built to SOC 2 Type II and ISO 27001 practice, behind a 99.99% uptime SLA.
What We Build
We build the AI layer of your product and the infrastructure underneath it. Autonomous agents that resolve real workflows end to end — orders, returns, claims, tickets — rather than scripted chatbots. LLM applications grounded in your own systems through retrieval, so an answer cites a source instead of inventing one. Fine-tuned and distilled models where latency or unit economics rule out a frontier API. Computer vision for inspection, document capture and safety monitoring. Streaming ML for decisions that have to land in milliseconds. Each one is built by senior engineers, wired into the stack you already run, and handed over with the evaluation suite, dashboards and runbooks that keep it working after we leave.
We start where the return is clearest. A short discovery maps your workflows against what current models can actually do, ranks candidates by value and data readiness, and retires the ones that do not survive contact with your data. What survives reaches a working prototype with a real evaluation rubric inside two weeks. Security and compliance are designed in from the first commit — zero-trust access, tenant isolation, PII handling and audit trails — because clients in finance and healthcare cannot retrofit them later. Follow-the-sun teams across 38 countries keep delivery moving in every timezone.
Key Deliverables
Every engagement ships artifacts you own, not slideware: the running system, the source, the evaluation suite that proves it clears threshold, and the operational documentation to run it without us. Twelve years of building has taught us that handover quality decides whether AI survives its first quarter.
- Production AI systems that meet agreed accuracy, latency and cost thresholds
- Agents and RAG pipelines wired into the stack and data you already run
- An evaluation suite and dashboards that make model quality measurable
- Guardrails, observability and runbooks your own team can operate
What’s included
- + Use-Case Discovery & Scoping
- + Model, RAG & Agent Build
- + Evaluation & Safety Testing
- + Deployment, MLOps & Monitoring
Process
From Idea
to Production
to Production
FAQs
Frequently Asked
Questions
Questions
Discovery takes three to seven days and a working prototype one to two weeks. Build, hardening and rollout follow, putting most v1 systems into production between six and ten weeks, depending on data access and compliance review.
A clear problem statement, success metrics, access to sample data, and a stakeholder who can make decisions. We’ll run a kickoff workshop to align scope.
We stay model-agnostic and choose per workload: frontier LLMs where reasoning matters, fine-tuned open models where latency or unit cost rules them out. Around that, Python, PyTorch, Kafka, vector search, and AWS, Azure or GCP — whichever cloud you already run.
Model and API spend is forecast during discovery and tracked per call, so you see the unit economics before the build starts. We size prompts, cache hard and distill where it pays — cost per call is an acceptance threshold, not an afterthought.
Contact
Let’s Build
What’s Next in AI
What’s Next in AI
E-mail address
hello@cryptonic.tech
Phone number
+1 (647) 555 0172