LLM API Integration
Integrate OpenAI, Claude, and other language models into your applications through production-grade APIs.
The API call is the easy part. The real work is everything around it. Prompts are versioned, so a wording change gets reviewed like code. Token budgets stop a runaway loop from becoming a five-figure bill. Retries and fallbacks cover a provider outage. Every response is validated before it reaches your database. The provider sits behind an interface, so switching between OpenAI, Anthropic or open source models is a config change, not a rewrite.
LLM integration services in depth ↗Machine Learning Model Deployment
Deploy ML models to production with scaling, monitoring, and continuous optimization built in.
A model that performs well in a notebook frequently disappoints in production, because live traffic does not resemble the training set. We deploy behind a versioned endpoint, log inputs and predictions so drift is measurable rather than anecdotal, and set up shadow deployment so a new version runs against real traffic without affecting anyone until its numbers justify the switch. Rollback is a deployment step, not an incident.
Data Pipeline Integration
Build ETL pipelines that feed AI systems with clean, structured, and continuously refreshed data.
AI projects usually fail on data, not models. We map the data flow between your systems first. Then we fix records that disagree and agree on one source of truth. Pipelines survive upstream schema changes and never double-count on a replay. Validation and alerts ship with the pipeline, because a quietly stale feed is worse than a broken one.
Real-Time AI Processing
Implement low-latency AI inference for real-time decisions inside live workflows.
Real time means a latency budget, and the budget is set by the person waiting. We work backwards from that number: what can be precomputed, what can be cached, what must be inferred live, and what the system does when the model is slower than the budget allows. Degrading gracefully to a simpler rule beats making a customer wait. We learned this the hard way integrating entry gates for a multi-location gym chain, where a three-second hesitation at peak hour is not a delay, it is a queue.
Legacy System AI Integration
Add AI capabilities to existing legacy systems without a costly, risky full rebuild.
The systems worth adding AI to usually cannot stop. So we work alongside them. An adapter layer reads through the interfaces you already have. We move one capability at a time, with a rollback path at every step. If a legacy platform has no usable API, we build one around it instead of changing it. Your system keeps working exactly as it does today.
Multi-Model Orchestration
Coordinate multiple AI models to work together for complex, multi-step business logic.
Multi-step AI workflows fail differently from single calls: errors compound, one weak step poisons everything downstream, and cost multiplies quietly. We make each step independently observable and independently retryable, define what happens when step three disagrees with step one, and route work to the cheapest model that meets the accuracy bar rather than sending everything to the largest one. Vendor independence matters here too, since routing across providers is only possible if none of them is wired into your business logic. Where those workflows need to act rather than answer, that is agent territory — see our AI agent development and integration work.
AI agent development and integration ↗
