Discover & Scope
Align on problems, data reality, and success metrics. Opportunity brief, KPI model, phased roadmap, effort/cost ranges.
AI Governance, Observability
& Evaluation
Continuous evaluation, hallucination detection, cost monitoring, and security guardrails to keep your production AI systems reliable, compliant, and cost-controlled.
Complete visibility and control over your production AI stack
Deploying AI models into production is only the beginning. Without continuous evaluation and real-time observability, silent model drift, unexpected hallucination rates, and runaway API costs can quickly erode user trust and burn budgets. We implement comprehensive LLM-Ops frameworks that track every prompt, completion, tool execution, and latency metric across your infrastructure.
We establish automated evaluation pipelines (evals) using deterministic assertions, LLM-as-a-judge patterns, and human-in-the-loop red teaming. Coupled with real-time prompt-injection defense, toxicity filtering, and automated cost capping, we give engineering leaders and compliance teams complete peace of mind to scale AI operations safely.
What We Do?
We build custom evaluation suites, real-time observability dashboards, and guardrail proxy layers for enterprise AI systems. We instrument end-to-end tracing across vector DBs, LLM calls, and agentic workflows using OpenTelemetry standards.
We set up automated regression testing for model updates, latency and cost monitoring dashboards, and security controls that protect against prompt injection and data leaks.
Key Deliverables
An enterprise-ready AI observability and governance layer providing real-time telemetry, automated quality evaluation, and automated cost and security controls.
- Automated evaluation platform (evals) testing accuracy, toxicity, and hallucination rates
- Real-time OpenTelemetry tracing pipeline tracking latency, token usage, and cost per request
- Inline security guardrails filtering prompt injections, jailbreaks, and sensitive PII leaks
- Governance dashboard with budget alert limits, drift detection, and compliance reporting
What's included ?
- +Observability & tracing instrumentation setup
- +Automated evaluation suite & benchmark creation
- +Guardrails & prompt security proxy implementation
- +Cost optimization & drift monitoring setup
Process
From Idea
to Production
to Production
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.
Contact
Let's build
something that ships
something that ships
Tell us the problem. If AI is the wrong tool for it, we will say so.