Deploy a fractional AI APIs team that designs, builds, and ships production-grade AI-powered APIs — connecting LLMs, models, and pipelines into reliable endpoints your product can depend on.
Building AI APIs in-house means your backend engineers spending months learning model quirks instead of shipping product. We've already solved those problems.
Building reliable AI APIs isn't just wrapping an LLM in a route handler. It's token budget management, graceful degradation, structured output enforcement, semantic caching, and production observability — all at once.
We bring battle-tested patterns from dozens of AI API deployments so you skip the expensive learning curve.
We implement embedding-based caching that cuts repeat AI inference costs by 40–70% without degrading response quality.
We implement JSON schema enforcement, retry-with-correction loops, and validation layers so your API always returns parseable, valid responses.
When a primary model is degraded or over-budget, we route to a fallback automatically — keeping your API SLA intact regardless of provider outages.
Specialized expertise deployed directly into your engineering pipeline.
We design and build the middleware layer between your application and AI providers — handling prompt engineering, context management, fallbacks, and cost optimization across OpenAI, Anthropic, and open-source models.
End-to-end REST and streaming API development with AI intelligence baked in — document extraction, classification, generation, and retrieval endpoints built to production reliability standards.
We deploy and scale custom models as production APIs — with load balancing, autoscaling, caching layers, and observability tooling included from day one.
We don't just write code and leave. We integrate seamlessly with your goals.
We map your use case, define the API surface, and design the architecture — including model selection, context strategy, and integration points.
We build the endpoints, implement the AI logic, and connect your data sources — with streaming, retry, and rate-limit handling included.
We run accuracy evaluations, load tests, and edge case analysis before the API goes near production traffic.
We deploy to your infrastructure with monitoring, alerting, and a structured handoff including full API documentation.
Every engagement ends with working software, documented systems, and a team that knows how to extend them. You own the intellectual property.
Versioned, documented, and deployed AI API endpoints ready for your frontend or product to consume — with auth, rate limiting, and error handling built in.
Latency, throughput, error rate, and AI-specific metrics — token usage, model fallback events, and hallucination flags — all wired to your monitoring stack.
Automated regression tests for AI output quality — so you can safely update models and prompts without degrading the API behavior your product depends on.
OpenAPI specs, integration guides, and runbooks so your engineering team can ship against the API and extend it without depending on us.
Real scenarios, real numbers. The specifics change — the pattern is consistent.
A lending platform needed to automate document verification across 15+ document types. We built an AI extraction API that classifies, extracts, and validates income documents with 94% accuracy.
A marketplace needed AI-powered product catalog enrichment — generating descriptions, tags, and attributes from raw supplier data. We built a batch API processing 10,000 SKUs per hour.
A clinical decision support tool needed a reliable LLM API with strict output formatting, fallback to safer models, and full audit logging for regulatory compliance.
Real engagements from this practice area — the challenge, the build, and the outcome.
Achieved 32% revenue growth, 28% faster ESG reporting, and 40% client retention in 6 months by solving data fragmentation and compliance challenges for textile sustainability reporting.
A leading US-based materials testing lab improved customer retention by 35% and captured 42% more enterprise leads within six months by deploying a domain-trained AI chatbot.
An India-based public cloud provider piloted an Agentic AI-driven competitive intelligence system for the ME region, delivering 45% faster insights, 35% better targeting, and driving 38% revenue growth.
The questions most teams ask us before they decide to move forward.
Ask us anythingWe work across the full stack — OpenAI, Anthropic, Google Gemini, Mistral, and open-source models via Ollama, vLLM, and Replicate. We select the right model for each capability and design the system to be swappable as the landscape evolves. We're not tied to any vendor.
We implement structured output enforcement, output validation layers, confidence scoring, and graceful fallbacks. For high-stakes use cases we add human-in-the-loop escalation paths. The evaluation test suite we deliver lets you measure hallucination rates continuously.
Yes. We design to your existing patterns — whether that's JWT auth, API keys, service mesh, or a custom gateway. We don't force new infrastructure on you.
For synchronous endpoints we target under 2 seconds P95 for most LLM tasks. For longer-running tasks we implement async patterns with webhook callbacks. Streaming responses typically deliver first-token in under 500ms. We profile and optimize against your specific SLA during the engagement.
Book a 30-minute strategy session. We'll map your specific opportunity, identify the highest-leverage starting point, and tell you exactly what an engagement looks like.
