AI Models & Integrations
Choosing a model is the easy part — routing requests, managing costs, handling fallbacks, and keeping latency predictable is where most AI projects stumble.
We integrate OpenAI, Anthropic, Mistral, and self-hosted Llama into Laravel, Node.js, and Python services with the abstraction layer your product needs to swap models without rewriting features.
What's included
Everything we deliver on this engagement
- Model evaluation against your real prompts and datasets
- Unified API layer across OpenAI, Anthropic, Azure OpenAI, and open models
- Latency and cost budgets with routing to cheaper models for simple tasks
- Fallback chains when primary providers rate-limit or fail
- Prompt versioning, A/B testing, and production logging
- On-premise or VPC deployment for data-sensitive workloads
- Fine-tuning and distillation when retrieval alone is insufficient
- Usage dashboards for finance and product teams
Our process
How we deliver ai models & integrations
- 01
Benchmark models
Side-by-side quality, speed, and cost tests on your actual use cases — not leaderboard hype.
- 02
Design the router
A service layer your app calls so model changes never ripple through every feature.
- 03
Integrate & secure
API keys in vaults, PII redaction, and access controls on inference endpoints.
- 04
Optimize spend
Caching, batching, and model downgrades for high-volume paths.
Tech stack
Tools we use for ai models & integrations
- GPT-4
- OpenAI API
- Python
- Node.js
- Laravel
- Docker
- Supabase
- Firebase
FAQ
Common questions about ai models & integrations
- How much does AI model integration cost?
- A production router with two providers and logging often starts at $8k–$15k. Full multi-model platforms with evals and on-prem options run higher.
- How long does model integration take?
- Basic OpenAI integration into an existing app can ship in 2–3 weeks. Multi-provider routing with fallbacks and dashboards typically needs 5–8 weeks.
- Can we use open-source models instead of GPT-4?
- Yes. We deploy Llama and Mistral on your infrastructure when data cannot leave your network or unit economics favor self-hosting at scale.
- How do you control OpenAI API costs?
- Token budgets per user, model routing, response caching, and alerts when daily spend exceeds thresholds you define.
Related
See this work in context
Ready to scope ai models & integrations?
Tell us about your product, timeline, and constraints. We reply within one business day with next steps — no generic pitch deck.
Your next big launch
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