AI Integration
We integrate large language models into real products responsibly — with evaluation frameworks, guardrails, cost controls, and observability built in. We work with Claude, OpenAI, and Gemini, and we choose models based on your use case, not familiarity. RAG pipelines, agent architectures, and tool-use systems are our specialty.
What's included
Model Selection
We evaluate models against your specific use case — latency, cost, quality, and capability all factored in. No defaults.
RAG Pipelines
Retrieval-augmented generation with vector databases, chunking strategies, and re-ranking. Your data, accurately surfaced.
Agent Architecture
Multi-step agents with tool-use, memory, and planning. Built to complete complex tasks reliably, not just impressively.
Evaluation Framework
Custom eval suites that measure what matters for your use case. No vibes-based quality assessment.
Guardrails
Input/output filtering, content policies, and safety layers that match your risk tolerance and regulatory environment.
Observability
Trace every LLM call, track costs, monitor latency, and alert on quality regressions. LLMOps from the start.
How we work
Use Case Scoping
Define the problem the AI should solve and the metrics that define success. Avoid building before you know what 'good' looks like.
Prototype & Eval
Rapid prototype tested against real data. Evaluation suite built alongside the prototype, not after.
Pipeline Build
Production-grade implementation with error handling, fallbacks, and cost controls.
Guardrails & Safety
Input validation, output filtering, and adversarial testing before any user-facing deployment.
Monitor & Iterate
Post-launch monitoring with regular quality reviews and model updates as the landscape evolves.
Common questions
It depends on your use case. We'll run a structured evaluation against your actual data and recommend based on results, not hype.
RAG grounds responses in your data. Eval suites catch regressions. Guardrails catch harmful outputs. No single solution — defense in depth.
We model inference costs as part of the design. Most integrations we build cost $0.01–$0.10 per user session at scale.
Yes. We audit the current implementation, identify the failure modes, and improve the pipeline systematically.
Why Piton Studios?
- Deep expertise in each discipline, not a generalist agency spreading thin.
- Transparent process — you know what's happening and why at every stage.
- Handover-ready delivery: documentation, training, and no black boxes.
The Process
- 01Use Case Scoping
- 02Prototype & Eval
- 03Pipeline Build
- 04Guardrails & Safety
- 05Monitor & Iterate
// Let's talk
Let's talk about this service.
Tell us about your project and we'll come back with a clear scope and timeline.