Perceptional: conversational AI for user research
- 12 weeks
- from concept to launch
LLM Integration Services
LLM integration that ships to production, not just the demo.
Language models are powerful general tools. Making them powerful for your specific business requires integration work that most dev teams underestimate.
We build LLM integration layers that connect language models to your data, your APIs, and your user workflows, with the prompt engineering, context management, and output handling that makes the difference between a demo and a production system.
OpenAI, Anthropic, Gemini, Llama, and Mistral integrations
Production-grade: rate limiting, fallbacks, token management, and monitoring
RAG pipelines, function calling, and structured output built to your spec
Shipping AI and LLM products to production since 2015
0-delay insights Voice AI20k+ txns day one AI Automation1,062 users in 4 weeks Loyalty
The problem
Sound familiar?
Your LLM prototype works in a notebook but breaks in production?
Model responses are inconsistent, the same question gives different answers?
Short answer
RaftLabs builds production-grade LLM integrations for businesses across the US, UK, Europe, Canada, and the UAE. We have shipped AI and LLM products to production since 2015 using OpenAI, Anthropic, Gemini, Llama, and Mistral. Every integration includes RAG pipelines, function calling, output validation, and monitoring.
Key takeaways
- We've shipped AI and LLM products to production since 2015 using OpenAI, Anthropic (Claude), Gemini, Llama, and Mistral.
- Every integration includes prompt engineering, failure handling, output validation, and production monitoring.
- We build RAG pipelines, function calling architectures, structured output extraction, and multi-step AI agents.
- Production LLM integrations require more than an API key: rate limiting, fallback handling, cost monitoring, and output validation all matter.
- We are model-agnostic: we recommend the right model for your use case based on cost, latency, accuracy, and data privacy.
Trusted by
The demo that worked in the notebook and broke in production.
Every LLM integration looks easy at first. You call the API, you get a response, the demo works. Then you run it at scale: responses drift, the model ignores instructions, token costs climb, the API times out under load, and someone asks it something it shouldn't answer and it answers anyway.
The prototype was never the hard part. The hard part is everything the notebook let you skip: prompt design that holds under real inputs, retrieval that grounds the model in your data, output that validates before it reaches your database, and monitoring that tells you what the model is doing and what it costs.
The interface is the least interesting part. The integration layer running underneath it is the product.
Production LLM integration is an engineering problem, not just an API call. The systems that run reliably in production are the ones where someone thought carefully about prompt design, failure handling, output validation, and monitoring before writing the first line of application code.
Adoption has moved fast: McKinsey's research found that the share of organizations regularly using generative AI jumped to roughly two-thirds in 2024, about double the year before, which is why the hard part is no longer whether to use an LLM but integrating one reliably.
RaftLabs has shipped AI and LLM products to production since 2015, for businesses across the US, UK, Europe, Canada, and the UAE. We are model-agnostic: OpenAI, Anthropic, Gemini, Llama, and Mistral, recommended for your use case on cost, latency, accuracy, and data privacy.
Proof
- Since 2015
- shipping AI and LLM products to production
- RaftLabs delivery record
- 5 model families
- model-agnostic across OpenAI, Anthropic, Gemini, Llama, and Mistral, chosen per use case
- Every LLM integration
- 4.9/5
- average client rating across delivered projects
- Clutch, verified reviews
Every integration ships with rate limiting, fallbacks, token management, and monitoring. GDPR, HIPAA, and SOC 2 requirements are scoped in week one, not retrofitted before launch: we have shipped HIPAA-compliant LLM systems for US healthcare clients and GDPR-compliant AI products for European markets. One team scopes the work at a fixed price, builds it, and hands it over.
LLM integration pays off when the demo has to become a system.
Everything on the left should already be true for your product. Even one thing on the right, and a hosted assistant or a single API call is the smarter first step.
A fit
01
An LLM prototype that already works in a notebook but breaks under production load, inconsistency, or cost.
02
Proprietary data, APIs, or workflows the model needs to reach through RAG, function calling, or structured output.
03
You need the integration to run reliably in production, with rate limiting, fallbacks, and monitoring, not just return a response once.
Not a fit
- A single API call with no data grounding, no tool use, and no production reliability requirements.
- You need a throwaway demo, not a system that has to stay accurate under real traffic.
- No proprietary data or workflow for the model to act on, so a hosted assistant already covers you.
What we build
What a production LLM integration includes
- 01RAG pipelinesEnd-to-end retrieval-augmented generation built on your actual data sources, not a prototype that only works on sample documents. We handle ingestion, chunking, embedding selection, and vector indexing, and default to hybrid search with cross-encoder re-ranking because it consistently beats dense-only retrieval on real query sets. Retrieval quality drives output quality, so we measure it explicitly before launch.
- 02Function calling and tool useLanguage models that call your APIs and internal tools to complete tasks from a natural language instruction, like looking up a customer, creating a ticket, or sending a message. We design function signatures so the model's tool selection stays accurate, and build handlers with retries, timeouts, and structured errors. Consequential actions like sending emails or triggering payments get human-in-the-loop checkpoints: the model proposes, a human authorises.
- 03Structured output extractionLLMs configured to reliably produce structured JSON that maps directly to your data model, so contract clauses, invoice fields, and document classifications land in your database without manual parsing. We enforce schemas at the model layer plus a validation layer that retries when output passes JSON parsing but fails business rules, and route low-confidence fields to human review. Accuracy depends entirely on your documents and your schema, so we measure it against your own labelled set before launch, not a vendor benchmark. On one receipt-extraction build, the validation layer reached roughly 99% accuracy, up from about 80% at the start (RaftLabs delivery, receipt-OCR loyalty platform).
- 04Multi-step AI agentsAI agents that reason through multi-step tasks autonomously from a single instruction, like researching a supplier, summarising risk flags, and drafting a memo for review. We build on an explicit graph structure that makes agent behaviour predictable and testable rather than emergent from unconstrained loops, with human-in-the-loop checkpoints before consequential actions and full tracing of every reasoning step. Failure modes like infinite loops and hallucinated tool inputs are addressed in the architecture, not discovered in production.
- 05Prompt engineering and optimisationSystem prompts engineered for consistency, accuracy, and token efficiency, not written once and assumed to work. Every prompt is evaluated against a labelled dataset of representative inputs before production, replacing subjective checks with measured format compliance and instruction-following rates. Prompts live in version control with their evaluation results, regression tests run on every deployment, and we re-evaluate against each new model version before migrating.
- 06LLM evaluation and monitoringEvaluation frameworks that measure whether your integration is working correctly and alert you before users discover it isn't. The evaluation dataset is built from real production queries, labelled with expected outputs, and automated evals run on every deployment, failing any release that drops accuracy beyond the defined threshold. Every call is instrumented with latency, token counts, error rates, and cost per feature or tenant, so "LLM costs are too high" becomes a specific, fixable number.
LLM integration services we offer
- 01
Generative AI integration
Add AI capability to software you already run. We wire language models into your existing APIs, endpoints, and user-facing features without a rebuild, so the product your team knows gains generation, summarisation, and extraction where it helps.
- 02
RAG pipeline development
Ground the model in your proprietary data with retrieval-augmented generation. Document ingestion, chunking, embeddings, vector indexing, hybrid search, and re-ranking, evaluated on your real queries before launch so answers are accurate and source-backed.
- 03
AI chatbot development
Production LLM chatbots that resolve routine queries end-to-end and hand off cleanly when they can't. Connected to your ticketing, CRM, and knowledge base, with guardrails and monitoring built in from the start.
- 04
Prompt engineering and evaluation
System prompts engineered for consistency, accuracy, and token efficiency, then measured against a labelled dataset before they reach production. Version-controlled prompts, regression testing on every deploy, and re-evaluation when a new model version ships.
- 05
LLM fine-tuning
When prompting and retrieval reach their limit, we fine-tune open and hosted models on your labelled data to lift accuracy on domain-specific tasks, tone, and format, with an evaluation harness that proves the gain before rollout.
- 06
Anthropic API integration
Claude integrations built around Anthropic tool use, structured output, and long-context reasoning, with fallback handling, cost monitoring, and streaming tuned for the workloads Claude fits best.
- 07
OpenAI and ChatGPT integration
GPT-based features wired into your product with Structured Outputs, function calling, and JSON schema enforcement, plus the rate-limit handling and caching a production OpenAI integration needs.
The stack we integrate LLMs with
We are model-agnostic and framework-agnostic. We pick the provider, orchestration layer, and retrieval stack that fit your accuracy, latency, cost, and data-privacy constraints, then document every choice so your team can maintain it. The technologies we reach for most often:
| Layer | Technologies we use | Where it fits |
|---|---|---|
| Model providers | OpenAI (GPT-4o, GPT-4 Turbo), Anthropic (Claude), Google (Gemini), Meta Llama, Mistral, Cohere | Reasoning, generation, extraction, and classification |
| SDKs and orchestration | Vercel AI SDK, LangChain, LlamaIndex, LangGraph | Streaming, tool calling, agent state, and prompt flow |
| Retrieval | Pinecone, Weaviate, pgvector, Qdrant, hybrid search, re-ranking | RAG over your PDFs, databases, and knowledge bases |
| Backend | Node.js, Python, FastAPI, NestJS | APIs, rate-limit handling, fallbacks, and output validation |
| Evaluation and monitoring | LangSmith, Langfuse, RAGAS, Pydantic validation | Quality evals, token cost tracking, and latency dashboards |
| Cloud and hosting | AWS Bedrock, Azure OpenAI, Google Cloud Vertex AI | Compliant, region-controlled, production-grade deployment |
The rule holds at every layer: no proprietary framework that locks you in, and no stack we cannot hand to your team on day one.
Prompt engineering, RAG, or fine-tuning: how we choose
Three approaches make an LLM work for your task, and most teams reach for the wrong one first. The choice drives cost, accuracy, and how much data you need. We start with the cheapest option that clears your accuracy bar and add complexity only when evaluation proves it pays.
| Approach | Best when | What it costs | What it can't do alone |
|---|---|---|---|
| Prompt engineering | The model already knows the domain and you need format, tone, and instruction control | Lowest, no extra infrastructure | Add knowledge the model never saw in training |
| RAG (retrieval) | Answers must reflect your private, changing data | A vector store plus retrieval tuning and evaluation | Fix reasoning or style gaps on its own |
| Fine-tuning | You need consistent tone, format, or a narrow task at scale | Labelled data plus training and an eval harness | Keep up with data that changes daily |
Most production systems combine all three: prompt engineering first, retrieval for knowledge, fine-tuning only when the first two hit a ceiling. We prove each step with an eval before adding the next.
Pitfalls we plan around
An LLM in production fails in ways a notebook demo never shows. We design for these from week 1, not after the first incident.
Hallucination. Models state wrong answers with full confidence. Stanford RegLab found even purpose-built legal AI tools hallucinate on 17% to 33% of queries (Stanford RegLab, 2024). We ground answers in your data with retrieval, cite sources back to the record, and route low-confidence output to human review.
Prompt injection. Untrusted input can hijack a model's instructions and misuse its tools. We separate system instructions from user content, constrain what each tool is allowed to do, and validate every tool call before it runs.
Cost blowout. Token spend scales with traffic and context size, quietly. We cache deterministic calls, route simple work to smaller models, and attribute cost per feature so a spike is traceable to a cause.
Provider lock-in. A single-provider integration breaks when pricing, rate limits, or model behaviour shift. We put a provider abstraction between your app and the model, so moving from one vendor to another is a config change, not a rebuild.
Where this is heading: agentic systems that plan and act across many tools, and the Model Context Protocol (MCP), an open standard from Anthropic for connecting models to your tools and data through one common interface. We build integrations that fit this direction, so today's function-calling work extends to MCP servers and multi-agent workflows without a rewrite.
LLM integrations we build by industry
The integration changes with the domain. Regulated sectors need PII scrubbing, audit trails, and access controls designed in from week 1, while high-volume operations need caching, smaller models, and cost attribution per request. We build LLM integrations for:
Healthcare and life sciences: HIPAA-ready RAG over clinical documentation, record coding, and patient-facing assistants with human review checkpoints.
FinTech and financial services: document extraction, risk summarisation, and compliance-aware assistants grounded in your own policies.
Retail and e-commerce: product search, catalogue enrichment, support automation, and recommendation copy at scale.
Logistics and supply chain: OCR pipelines for receipts and manifests, exception triage, and operational query assistants.
Insurance: claims and policy document extraction, underwriting support, and structured output that maps to your systems.
Legal and professional services: contract clause extraction, due-diligence summarisation, and retrieval grounded in your matter documents.
What LLM feature works in the notebook but not in production?
Walk us through where it breaks down: inconsistent output, latency, cost, or reliability. We'll tell you what production-grade would take.
How it works
From scope to shipped
Every LLM integration follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 101
Discovery and scope
We map the problem, the data sources, and the user workflow. You leave week 1 with a written scope document and a fixed-price quote. We identify which model, retrieval pattern, and output schema fits your use case. No development starts without your sign-off.
- Weeks 2-302
Prototype and architecture
A working prototype against your real data before any production code. We validate retrieval quality, prompt behaviour, and output consistency on representative inputs. Architecture decisions made here cost ten times less than the same decisions made in week 8.
- Weeks 4-1203
Build, integrate, and QA
Production integration with your APIs, authentication, rate-limit handling, fallback logic, and output validation in place. Bi-weekly demos. Automated eval suite runs in parallel with every sprint, not as a phase at the end.
- Weeks 12+04
Deploy and monitor
Production deployment with LangSmith or Langfuse monitoring activated on launch day. Token cost tracking, latency dashboards, and error rate alerting from day one. 8 weeks of post-launch support included in every project.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
01 / 02
Proof
Case studies
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Read moreFrequently asked questions
LLM (Large Language Model) integration is the process of connecting a language model API to your application, data, and workflows in a production-ready way. This includes designing prompts that produce consistent output, building retrieval systems so the model can use your data, handling rate limits and failures gracefully, parsing and validating model output, and monitoring the system in production. It's the engineering work between "the API works" and "this is running reliably in production."
We've built production integrations with GPT-4o and GPT-4 Turbo (OpenAI), Claude 3.5 Sonnet and Claude 3 Haiku (Anthropic), Gemini 1.5 Pro and Flash (Google), Llama 3.1 8B, 70B, and 405B (Meta/Groq), Mistral Large and Mixtral (Mistral AI), and Cohere Command R+. Model selection depends on the use case, we recommend based on context window, cost, latency, and reasoning requirements.
RAG (retrieval-augmented generation) is a pattern where the model retrieves relevant information from your data before generating a response. Instead of relying on what the model learned during training, it looks up the relevant documents, database records, or knowledge base articles for the specific query, then uses that retrieved context to generate an accurate, source-backed response. You need RAG when your application requires accurate information about your specific business, products, or data that the model wouldn't otherwise know.
Inconsistency is the primary production challenge with LLMs. We address it through structured output modes (JSON schema enforced by the model or validated by a parsing layer), few-shot examples in the system prompt that show the model exactly what format you want, output validation that retries the call with corrected instructions when the format is wrong, and temperature and sampling settings tuned for your task (lower temperature for factual extraction, higher for creative tasks).
LLM latency is real, a GPT-4 call can take 10-30 seconds for long outputs. We design around it: streaming responses that show output as it's generated (so users see something immediately), caching for deterministic queries that always return the same answer, smaller/faster models (Claude Haiku, GPT-4o Mini, Gemini Flash) for latency-sensitive tasks, and async processing for tasks where real-time response isn't required. We profile latency during build and design the UX around it.
We instrument LLM integrations with request and response logging (with PII scrubbing where required), latency and error rate tracking, token usage monitoring (for cost management), model version tracking, and output quality sampling. We use LangSmith, Langfuse, or custom logging depending on the scale and complexity of the integration. You can see what the model is doing, what it costs, and where it's failing.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope LLM Integration Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.