Applied AI products
Applied AI product engineering for agents, retrieval, model workflows, evaluation, and production software.
Who this is for
This is for you if an AI prototype works in a demo but needs a reliable, measurable path into production.
What it is
Applied AI product engineering combines product judgment, software engineering, models, data, and evaluation. The system around the model makes behavior testable, operable, and useful in a real workflow.
Problems we work on
- An AI prototype needs a reliable production path
- Model behavior is hard to measure against the actual user task
- Agents or retrieval systems fail on edge cases and real data
- The product must work across models without locking the architecture to one vendor
- Latency, quality, privacy, and cost requirements pull the design in different directions
The work
- Design the product workflow around a measurable user outcome
- Connect models to retrieval, tools, permissions, and business logic
- Build deterministic controls around probabilistic behavior
- Create evaluation, observability, and feedback paths
- Harden latency, cost, reliability, safety, and operational controls
What it costs to run
Estimate the monthly cost of an AI feature at your volume.
per month at 2,000 tasks a day — people alone would cost $120K
AI system, built right — Doing it with people —
Your numbers, not a quote. The AI line assumes per-task cost falls with volume through caching, batching, and model routing — the engineering we do. People costs scale in a straight line.
How the engagement runs
- A small team is composed around the work instead of a fixed staffing shape
- The engagement runs on a monthly cadence with named outputs and exit criteria
- It ends with a documented handoff to your team or continued operation
How Aravali Labs works
- Choose models and tools around the task rather than a fixed vendor
- Treat evaluation as part of product engineering
- Use routing, fallbacks, and human review where risk requires them
- Separate model configuration from product and data contracts
- Move from a narrow production workflow toward reusable capability
Outputs
- A defined user workflow with measurable success criteria
- A production architecture across models, tools, data, and controls
- Evaluation sets, trace capture, and operating dashboards
- Fallback, escalation, and human-review behavior
- A cost and latency envelope for normal and peak operation
What to measure
- Task success and critical failure rates by user and workflow slice
- End-to-end latency rather than model latency alone
- Cost per completed task and cost of retries or escalation
- Human intervention rate and quality after intervention
- Production drift across model, prompt, data, and tool changes
Questions
- What is applied AI product engineering?
- It is the work of turning models into useful software through data, tools, interfaces, evaluation, infrastructure, and production operation.
- Can the architecture use different model providers?
- Yes. Model access can be separated from product logic so quality, latency, cost, privacy, and control can determine routing.
- Is the running-cost estimate a quote?
- No. It runs on your assumptions. Real cost depends on the model, the workload, and how much human review the product needs.