AI integration services that make LLMs reliable in your product, not just impressive in a demo
Innostax integrates large language models into your existing product — with the retrieval architecture, multi-model orchestration, and confidence scoring that separates a production AI feature from a prototype. A dedicated Tech Lead owns the implementation and the outcome.
LLM Integration Problems
Calling an API is the easy part. Making the output reliable is the engineering.
Integrating an LLM into a product looks straightforward from the outside. Call the API, pass the prompt, display the response. A developer can have something working in an afternoon.
The production problems start immediately after.
The model produces outputs that are correct 95% of the time and confidently wrong 5% of the time — and you have no way to tell which is which before the output reaches the user. The prompt that worked perfectly in development produces inconsistent results when real users provide inputs you didn’t anticipate. Latency that was acceptable in a sandbox is unacceptable in a product feature users are paying for.
The cost model that seemed fine for a prototype becomes untenable when the feature scales. Context windows fill up and the model loses track of earlier information in ways that are hard to reproduce and harder to debug.
These aren’t edge cases. They’re the standard failure modes of LLM integration done without the engineering discipline that production requires.
Innostax builds LLM integrations with that discipline from the start — retrieval-augmented generation grounded in your data, multi-model orchestration that routes tasks to the right model, confidence scoring that catches unreliable outputs before they reach users, and the observability layer that tells you when the system is drifting.
What we build with AI integration
Retrieval-Augmented Generation (RAG)
The architecture that grounds LLM outputs in your actual data — so the model answers from what you know, not from what it was trained on. We build RAG systems with the vector database architecture, embedding strategy, chunking design, and retrieval quality evaluation that makes grounded AI outputs reliable in production. The difference between an LLM that hallucinates and one that doesn’t is almost always the quality of the retrieval layer.
Multi-model orchestration
Provider-agnostic LLM architectures that route tasks to the appropriate model based on complexity, latency requirements, and cost. Claude for high-reasoning tasks and large context windows. GPT-4o for rapid completions and embeddings. Gemini for high-throughput processing. The right model for each task in the pipeline — not the same model for everything because it’s simpler to manage. And a switching layer that lets you adopt better models as the landscape evolves without rebuilding the system.
LLM-powered product features
AI features built into your existing product — intelligent search, document summarisation, content generation, conversational interfaces, classification and routing — with the prompt architecture, context management, and output validation that makes them reliable at scale. Not features that work in a demo. Features that work when real users use them in ways you didn’t anticipate.
Conversational AI interfaces
Chat interfaces, AI assistants, and conversational features built on LLMs — with the conversation state management, context window handling, and session architecture that keeps multi-turn conversations coherent. For teams adding conversational AI to their product without rebuilding the application layer.
Prompt engineering and management
Structured prompt design, prompt versioning, and the evaluation framework that tells you whether a prompt change improved or degraded output quality. Prompt engineering treated as a software engineering discipline — not a collection of strings scattered across a codebase.
Output validation and guardrails
Confidence scoring, output format validation, content filtering, and the escalation logic that routes low-confidence or out-of-scope outputs to human review rather than passing them through to users. The guardrail architecture that makes AI features safe to ship in production — especially in regulated industries where a hallucinated output has compliance implications.
LLM cost optimisation
Token usage optimisation, model tier selection, caching strategies for repeated queries, and the cost monitoring that prevents an AI feature from becoming an unexpectedly expensive line item. Production AI cost management as an engineering discipline, not an afterthought.
Architecture decisions made before the first API call.
Use case definition before model selection. Before we select a model or write a prompt, the Tech Lead leads a structured discovery — defining exactly what the AI is doing, what inputs it receives, what outputs it produces, what the acceptable failure modes are, and what “good” looks like for this specific use case. Model selection follows from the use case. Not the other way around.
RAG design before implementation.
For any LLM feature that needs to draw on your data — which is most of them — we design the retrieval architecture before implementation begins. What data needs to be indexed. How it should be chunked. Which embedding model fits the retrieval task. How retrieval quality is evaluated. Getting this right before implementation prevents the rebuild that happens when a RAG system produces unreliable retrievals at production scale.
Evaluation framework from day one.
How do you know if your LLM integration is working? We define the evaluation criteria — output quality metrics, retrieval accuracy, latency benchmarks, cost per query — before implementation begins and measure against them throughout. Not a subjective “does it look good” assessment. A structured evaluation that tells you whether the system is improving or degrading as you iterate.
Incremental production rollout.
LLM integrations go to production incrementally — starting with low-risk use cases, measuring performance against the evaluation framework, and expanding scope as confidence in the system grows. Not a big-bang release of an AI feature that hasn’t been validated under real production conditions.
Observability built in.
Output logging, quality metrics, latency tracking, cost monitoring, and drift detection — configured before the feature goes live. The observability layer that tells you when the system is behaving differently from how it was designed, before users start complaining.
LLM Integration for Production, Not Just Development
2-week free trial on real work.
Your use case, your data, your actual product. You’ll see within two weeks whether the integration holds up under real conditions or whether the production failure modes are already visible. If the latter, walk away. No invoice.
1-day termination notice.
If the integration isn’t delivering reliable AI outputs in production, you’re out tomorrow. No lock-in, no notice periods.
Engineers who stay for the full build.
Great Place to Work certified — the engineer who designs your retrieval architecture in month one is still accountable for its performance in month six. LLM integrations require continuity. Prompt engineering, retrieval tuning, and model updates are iterative — they require an engineer who understands the system’s history, not one who’s rediscovering it.
LLM Integration Case Studies and Real-World Results
Real-time AI coaching for sales calls
A multi-model LLM integration for live call analysis — Claude 3.5 for real-time objection detection, GPT for embeddings, AssemblyAI for streaming transcription — with outputs grounded in the client’s internal knowledge base and delivered via SignalR with zero-latency push to the agent’s dashboard. The system detects critical conversation turns and generates resolution suggestions in real time. Three LLM providers, each used for what it does best, orchestrated in a single production system.
AI-powered candidate matching
Semantic matching using Hugging Face sentence transformers and custom OpenAI models — vector embeddings in PostgreSQL and Elasticsearch powering similarity scoring that explains why a candidate matches a role. Azure AI Foundry for heavy processing, custom models for profile generation. LLM integration at the scale of thousands of concurrent job-candidate pairs, with explainable outputs rather than black-box scores.
Multi-model document processing for banking compliance
An LLM pipeline using AWS Bedrock — Amazon Nova, Writer Palmyra X5, and the Claude family — for automated compliance checklist completion from appraisal documents. Multi-step validation across models catches low-confidence outputs before they reach reviewers. Every answer includes evidence. Every step is auditable. LLM integration in a regulated environment where output reliability is non-negotiable.
AI integration for product teams that need production-grade LLM systems
CTOs and engineering leads at B2B SaaS companies adding AI features to an existing product.
You know what you want to build. You need an engineering team that can build it reliably — with the retrieval architecture, guardrails, and observability that make it production-grade rather than prototype-grade.
Product teams with a specific AI use case
Intelligent search, document summarisation, conversational interface, content generation — who need an LLM integration built correctly the first time rather than rebuilt after the first production failure.
FinTech and HealthTech teams
where LLM outputs have compliance implications. You need AI integration built with the confidence scoring, output validation, and audit logging that regulated industries require — not retrofitted after a compliance review finds the gaps.
Tech stack
AI Integration and LLM Development Tech Stack
We use modern LLMs, vector databases, orchestration, and cloud tools to build scalable AI applications and integrations.
FAQ about AI Integration Services & LLM Development
It depends on the task. Claude is our default for high-reasoning tasks, complex instructions, and large context windows — it handles nuanced, multi-step reasoning better than most alternatives. GPT-4o is optimised for rapid completions, function calling, and embeddings. Gemini is strong for high-throughput processing. For most production systems, the right answer is a provider-agnostic architecture that uses each model for what it does best — not a single-provider commitment that limits your options as the landscape evolves.
Retrieval-Augmented Generation is the architecture that grounds LLM outputs in your specific data — your documentation, your product knowledge, your customer data — rather than relying on what the model was trained on. If your AI feature needs to answer questions accurately about your specific domain, or if hallucination is a risk you can't accept, you need RAG. The retrieval layer is what separates a general-purpose LLM from one that's reliable for your specific use case.
Through retrieval-augmented generation (grounding outputs in your data), confidence scoring (flagging low-reliability outputs for human review), output format validation (catching structurally incorrect outputs before they reach users), and multi-step validation for high-stakes pipelines (cross-checking outputs across models). The right combination depends on your use case and acceptable failure rate.
Token usage optimisation (prompt compression, context window management), model tier selection (using smaller, faster models for simpler tasks), caching for repeated queries, and cost monitoring that alerts on unexpected spend. We design cost management into the integration from the start — not as an afterthought when the first bill arrives.
Yes — and it's the most common scenario. LLM integration is additive in most cases: a new service layer that connects your existing application to LLM providers, with the retrieval and orchestration architecture built alongside your existing infrastructure. We integrate with your existing data stores, your existing APIs, and your existing deployment pipeline.
A focused LLM feature — intelligent search, document summarisation, a conversational interface — can be production-ready in four to six weeks with clean underlying data. A more complex integration with multi-model orchestration, RAG across multiple data sources, and compliance requirements typically takes eight to twelve weeks. We'll give you a realistic estimate after the discovery phase.