LLM Integration Agency for B2B SaaS

LLM Integration Agency at Global One Digital ships production-grade LLM features into B2B SaaS products and internal tooling. We work with OpenAI, Claude, and Gemini APIs, building proper RAG pipelines, evaluations, prompt versioning, and observability — not prototypes that break the moment real users hit them.

Where LLM integration adds the most value

SaaS companies adding AI features to existing products — summarisation, drafting, structured extraction, conversational interfaces. B2B teams building internal tools that automate document processing, customer support triage, or knowledge retrieval over corporate data. Operators replacing rigid rule-based systems with LLM workflows when business logic is complex enough that hand-coded rules cannot keep up.

What is included in our LLM engagements

Standard scope: use case discovery and feasibility scoping, model selection (OpenAI versus Claude versus Gemini based on task), RAG architecture with proper embedding strategy and vector database choice, prompt engineering with versioning and evaluations, structured output handling, observability setup (logging, latency, cost tracking), plus evaluations harness so the team can ship prompt and model changes safely. We treat LLM features like real software, not magic.

Stack and approach

Models: Claude for nuanced reasoning and longer context, OpenAI for cost-sensitive high-volume tasks and broad ecosystem support, Gemini for multimodal use cases. Vector databases: pgvector for teams already on PostgreSQL, Pinecone for scaled production, Weaviate or Qdrant for self-hosted. Frameworks: LangChain or LlamaIndex when they fit, plain SDK calls when frameworks add more confusion than value. Evaluations via Promptfoo or custom harnesses. Observability via Helicone or self-hosted logging.

Realistic timelines

Discovery and feasibility sprint (2 weeks): use case scoping, model and architecture choice, evaluation criteria definition. Build sprints (4-8 weeks total): RAG pipeline, prompt engineering, structured outputs, evaluations harness, observability. Pilot deployment (2 weeks): controlled rollout to a subset of users with monitoring. Most LLM features ship to production in 8-12 weeks. Complex multi-step agent systems run 14-20 weeks.

Who this is designed for

B2B SaaS teams adding AI features to existing products and wanting them to work in production. Companies building internal LLM tools for document processing, support automation, or knowledge retrieval. Engineering teams who tried prototypes that demoed well but failed under real user load. Operators replacing brittle rule-based systems with LLM workflows. Anyone tired of LLM consultants who ship demos rather than software.

Why specialised over generic AI consultants

LLM integration has specifics generic AI consultants miss: production observability and cost tracking, evaluations harnesses that catch regressions, prompt versioning matched to deployment pipelines, RAG architecture choices that match data and query patterns. Generic consultants ship prototypes that look impressive in demos but break in production. We build LLM features like the production systems they need to be.