AI Model Integration
Choosing a model is ten percent of the work. Integrating it so it is fast, cheap, accurate, and safe is the other ninety.
Model capabilities change monthly; integration mistakes are expensive for years. We integrate frontier APIs and open-weight models into your products and internal systems with the layers that make them dependable: prompt and context engineering, retrieval, caching, evaluation harnesses, and cost routing. The result is a model layer you can swap, scale, and afford - not a demo wired directly into production.
The Challenge
Your model API bill doubled last month and nobody can explain why.
Response latency is killing the user experience at peak load.
The model you picked wins benchmarks and loses on your actual data.
Regulatory or client requirements mean your data cannot leave your own infrastructure.
Our Approach
We benchmark candidate models against your real tasks, data, and latency budget before writing any integration code. Selection is evidence, not instinct.
Integration means the boring parts done right: retries, fallbacks, rate-limit handling, caching, and routing between models by cost and quality. For data-sovereignty requirements we deploy and tune open-weight models on your infrastructure. Dashboards show cost and quality per request, so the bill never surprises you again.
Capabilities
Model selection and benchmarking: Candidate models tested against your actual tasks, data, and latency budget.
API integration: Clean integration of frontier model APIs into your product, with retries, fallbacks, and rate-limit handling.
Open-weight deployment: Self-hosted models for data-sovereignty or cost reasons, tuned and served on your infrastructure.
Context and retrieval layers: RAG and tool-use layers that ground model output in your real data.
Cost and latency engineering: Caching, routing, and distillation so the bill and the response time stay under control.
Execution Process
Benchmark
Candidate models evaluated on your tasks with your data.
Integrate
The chosen model wired in behind a clean abstraction layer.
Evaluate
Accuracy, latency, and cost measured against agreed thresholds.
Optimize
Routing, caching, and prompt tuning until the numbers hold in production.
Business Outcomes
Model choice is proven on your tasks, not vendor benchmarks.
The abstraction layer lets you swap models as the market moves.
Cost per request is engineered and visible, not a monthly surprise.
Accuracy is measured by an eval suite, not by vibes.
Data stays where your compliance needs it - API or self-hosted.
Deliverables
Technologies
Relevant Industries
Related Services
Frequently Asked Questions
It depends on your data constraints, volume, and latency budget. The benchmark phase answers it with your numbers, not our preference.
Yes - that is the core of this service. We work inside your current stack and hand your engineers a clean integration layer.
The abstraction layer means swapping models is a configuration change plus a regression run, not a rebuild.
AI Model Integration
The right model, wired into your stack correctly - APIs, open-weight models, and everything in between.
Plan an integration