LLM Integration Services

End-to-end retrieval-augmented generation systems built on your actual data sources, PDFs, relational databases, REST APIs, websites, SharePoint, Confluence, and Notion, not a generic prototype that works on sample documents. Document ingestion handles format extraction (PyMuPDF, python-docx, HTML parsers with boilerplate removal) and then chunking strategy selection based on document structure: fixed-size windows for homogeneous text, paragraph-boundary splits for reports, hierarchical chunking (chunk + parent retrieval) for documents where individual chunks lack sufficient context. Embedding generation uses text-embedding-3-large or domain-specific fine-tuned models for high-accuracy retrieval; text-embedding-3-small for cost-sensitive, high-volume pipelines. Vector store indexing in Pinecone, Weaviate, pgvector, or Qdrant depending on your operational preferences and scale requirements. Hybrid search (dense vector + sparse BM25) with Reciprocal Rank Fusion merging, followed by cross-encoder re-ranking for the top-K candidates, consistently outperforms dense-only retrieval on real-world query sets. RAGAS four-dimension evaluation (Faithfulness, Answer Relevance, Context Precision, Context Recall) measured against a held-out query set before launch, retrieval quality is the primary driver of RAG output quality, and we evaluate it explicitly.

Language models that call your APIs and use your internal tools to complete tasks, an AI assistant that can look up a customer record in Salesforce, check inventory in NetSuite, create a support ticket in Zendesk, send a Slack message, execute a database query, or trigger a workflow, all from a natural language instruction. Function signatures are designed to make the model's tool selection accurate: overly broad function descriptions produce incorrect tool calls; too-narrow schemas prevent legitimate uses. Tool handlers implement proper error handling, retry logic with exponential backoff, timeout management, and structured error responses that the model can reason about rather than silently failing. Guardrails prevent the model from calling tools it shouldn't in edge cases: a read-only assistant that shouldn't trigger write operations gets tool definitions that exclude write endpoints rather than relying on prompt instructions that can be overridden. For tools that take consequential actions (sending emails, creating records, triggering payments), human-in-the-loop confirmation checkpoints require approval before execution, the model proposes, the human authorises. OpenAI function calling, Anthropic tool use, and Gemini function declarations each have different JSON schema requirements and behavioural characteristics; we select and configure based on your primary model.

LLMs configured to reliably produce structured JSON output that maps directly to your application data model, so contract clause extraction, invoice field parsing, medical record coding, form auto-fill, and document classification produce outputs that are schema-validated, typed, and ready to insert into your database without a manual parsing or cleaning step. OpenAI's JSON mode enforces valid JSON syntax but not schema compliance; the Structured Outputs feature (using JSON Schema with strict: true) enforces exact field names and types. Anthropic's tool use pattern forces structured output by framing extraction as a tool call with a defined input schema. Pydantic-based validation layers provide a model-agnostic schema enforcement layer with automatic retry logic when the model produces output that passes JSON parsing but fails business-rule validation (a date field that contains "Q1 2024" instead of an ISO date string). For high-confidence extraction tasks, confidence scoring prompts the model to assign a field-level certainty rating alongside each extracted value, enabling downstream logic to route low-confidence fields to a human review queue rather than auto-posting them. Average field-level accuracy for invoice extraction on typical document sets: 94-97% with a single-pass LLM approach; 97-99% with a validation+retry layer.

AI agents that reason through multi-step tasks autonomously, research a supplier across public filings and news sources, summarise risk flags, draft a due diligence memo, and route it to the procurement lead for review, all from a single instruction with no human managing the intermediate steps. LangGraph provides the state machine foundation for agent workflows: explicit node-and-edge graph structure makes agent behaviour predictable and testable rather than emergent from unconstrained ReAct loops, and supports conditional branching (different tool paths depending on intermediate results), parallel node execution (simultaneous lookups across multiple data sources), and cycle detection to prevent the infinite tool-call loops that plague simpler agent frameworks. Human-in-the-loop checkpoints are placed before consequential actions (sending communications, creating records, triggering financial transactions), the agent proposes the action with its reasoning, a human approves or redirects, and execution proceeds. Agent observability via LangSmith or Langfuse traces every reasoning step, tool call, and intermediate result so failures are diagnosable rather than mysterious. We scope agent failure modes before build: the loops, the hallucinated tool inputs, and the context divergence that occurs when agents run for many steps are addressed in the architecture, not discovered in production.

System prompts engineered for consistency, accuracy, and token efficiency, not written once and assumed to work. The difference between a prompt that produces the correct output 70% of the time and one that produces it 95% of the time is usually in the system prompt structure: role definition that sets the model's persona and constraints, few-shot examples that show the exact format and reasoning style required, negative examples that demonstrate what not to do, and explicit handling of edge cases that otherwise produce inconsistent output. Prompt evaluation runs against a labelled dataset of 50-200 representative inputs before a prompt goes to production; quality metrics (format compliance rate, factual accuracy on knowable facts, instruction-following rate) replace subjective "it looks right" assessment. Prompts are stored as version-controlled artifacts in your codebase alongside the evaluation results, so you can see what the prompt looked like when it achieved 97% accuracy vs. the current version. Regression testing runs the current prompt against the evaluation dataset on every deployment, catching quality degradation before it reaches users. When OpenAI, Anthropic, or Google releases a new model version, we re-evaluate your existing prompts against the new model before migration, prompts optimised for GPT-4 Turbo often need adjustment for GPT-4o because model behaviour differences affect instruction-following in subtle ways.

Evaluation frameworks that measure whether your LLM integration is working correctly, and alert you before users discover it isn't. The evaluation dataset is built from real production queries sampled from your logs, labelled with expected outputs by domain experts, and stratified to cover the query distribution rather than only the easy cases. Quality criteria are defined per use case: factual accuracy for knowledge retrieval (is the stated fact correct and sourced?), format compliance for structured extraction (does the output match the schema?), instruction-following rate for task completion, and hallucination rate for RAG systems (does the response cite content that exists in the retrieved documents?). Automated evals run on every deployment via CI/CD integration, a deployment that drops the accuracy metric by more than the defined threshold (typically 2-3%) fails automatically and triggers review before it reaches production. Production monitoring via LangSmith or Langfuse instruments every LLM call with latency (p50, p95), input/output token counts (for cost attribution), model version, error rate (rate limit errors, timeout errors, refusal rate), and a quality sample that logs input-output pairs for human review on a configurable sampling rate. Cost attribution shows token usage and spend per feature, per tenant, or per request type, so "LLM costs are too high" becomes "the batch summarisation feature costs $0.18 per document and we can reduce that by 60% by switching to Haiku for first-pass summarisation."