AI & Automation

LLM integration that ships AI features your users actually use

Production-ready OpenAI, Anthropic, and Gemini integrations - prompt engineering, context management, RAG pipelines, and multi-model orchestration for real product workflows.

✦Production-ready

◈AI & Automation

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LLM Integration

How we use it

We integrate Claude (Anthropic), GPT-4o (OpenAI), and Gemini depending on the use case. For document and semantic workflows we build RAG systems with pgvector in PostgreSQL. For multi-step agent workflows we use the Anthropic tool use API. All LLM costs are tracked with token-level logging from day one.

Best fit for

AI-powered SaaS features

Document intelligence

Conversational interfaces

Why now

Global spending on generative AI reached $644 billion in 2025 - a 76.4% jump from 2024 (Gartner). 78% of organisations now use AI in at least one business function (McKinsey, 2025) and the LLM-powered app count is projected to reach 750 million globally in 2025. Yet MIT research shows 95% of generative AI pilot programmes fail to achieve production scale. The gap is not capability - it is engineering. Most AI features fail because of poor prompt design, missing context management, no evaluation framework, uncontrolled costs, and no fallback when the model returns unexpected output. We build LLM integrations that ship and stay in production.

What's included

Capabilities

LLM integration & prompt engineering

RAG system design & vector database setup

Workflow automation & agent orchestration

Custom AI pipeline architecture

Evaluation, monitoring & cost optimisation

Fit analysis

Is this right for you?

Honest breakdown of where LLM Integration shines — and where it doesn't. Pick the right tool.

When to choose this

Right fit scenarios

You want to add AI-powered features to an existing SaaS product - document summarisation, content generation, intelligent search, or Q&A over your data - and need production-grade reliability, not a prototype

Your business processes involve large volumes of unstructured text - emails, support tickets, contracts, reports - that currently require manual reading and decision-making

You are building a product where the core value proposition is AI-powered - a writing assistant, a legal document analyser, a personalised learning system, or a conversational customer interface

You want to implement semantic search over your product data so users can find information using natural language rather than exact keyword matches

You are exploring how to use AI agents to automate multi-step internal workflows - research, data extraction, report generation, or decision routing - that currently require human coordination

When to choose this

Right fit scenarios

Your business processes involve large volumes of unstructured text - emails, support tickets, contracts, reports - that currently require manual reading and decision-making

You are building a product where the core value proposition is AI-powered - a writing assistant, a legal document analyser, a personalised learning system, or a conversational customer interface

You want to implement semantic search over your product data so users can find information using natural language rather than exact keyword matches

You are exploring how to use AI agents to automate multi-step internal workflows - research, data extraction, report generation, or decision routing - that currently require human coordination

Honest limitations

Not the best fit if…

Use cases that are better served by a simple rule-based system or decision tree - if the logic is deterministic and enumerable, LLMs add cost and unpredictability without benefit

Applications with zero tolerance for hallucination or approximate answers - mission-critical financial calculations or medical diagnosis decisions should not delegate to language models without strict verification layers

Teams with no existing product or data infrastructure - LLM integration compounds existing systems, it does not substitute for them

Organisations not ready to invest in ongoing evaluation and monitoring - LLM behaviour can drift with model updates, and without measurement you will not know when it does

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Common questions

You're probably wondering

Which LLM should I use - Claude, GPT-4o, or Gemini?

As of mid-2025, Anthropic's Claude holds 32% enterprise market share - ahead of OpenAI at 25% and Google at 20% (Menlo Ventures). Claude is our default for code generation, complex reasoning, and tasks requiring nuanced understanding of long documents. GPT-4o is strong for multimodal tasks and tool use with structured outputs. Gemini integrates well with Google Workspace and is cost-effective for high-volume classification tasks. Most production systems use 2+ models: the right model for the right task.

What is RAG and does my product need it?

RAG (Retrieval-Augmented Generation) grounds LLM responses in your specific data. Instead of answering from training knowledge alone, the model first retrieves relevant chunks from your documents, database, or knowledge base - then generates an answer using that context. You need RAG if your AI feature needs to answer questions about your company's documents, your product's data, or information that changes after the model's training cutoff. Without RAG, the model guesses. With RAG, it uses your data.

How do you prevent LLM hallucinations in production?

We use a layered approach: RAG to ground responses in verified sources, structured output parsing (Zod schemas applied to model outputs) to enforce response format, confidence scoring and uncertainty detection in prompts, human-in-the-loop checkpoints for high-stakes decisions, and automated evaluation against a test set before any prompt change goes to production. Hallucinations cannot be eliminated entirely, but they can be detected and mitigated.

How much does LLM API usage cost and how do you control it?

Costs vary by model and volume. GPT-4o input costs $2.50/million tokens, Claude Sonnet costs $3/million input tokens. A typical feature that processes 1,000 requests per day at 2,000 tokens each costs $150–300/month in API fees. We implement token-level logging from day one, set per-request token limits, cache common responses with semantic similarity matching (reducing redundant API calls by 40–60%), and set up budget alerts that fire before costs spike.

How long does it take to build and ship an LLM feature?

A focused LLM feature - document summarisation, semantic search, or a conversational interface - takes 4–8 weeks to build properly: prompt engineering, RAG pipeline if needed, error handling, evaluation framework, cost tracking, and production hardening. A full AI-powered product module with multiple capabilities takes 12–20 weeks. Prototypes can be demonstrated faster, but production-ready shipping with monitoring and fallbacks takes the full timeline.

What is prompt engineering and why does it matter?

Prompt engineering is the practice of designing the instructions, context, and constraints you pass to a language model to reliably get useful outputs. A poorly designed prompt returns inconsistent, expensive, or wrong results. A well-engineered prompt system defines the model's role, constrains its output format, handles edge cases, and produces predictable results across the full distribution of user inputs. We treat prompts as first-class code - version-controlled, tested, and reviewed like any other production system.

Can you build AI agents that take autonomous actions?

Yes. We build agentic systems using Anthropic's tool use API (and OpenAI function calling) that allow models to call external APIs, read and write databases, run code, and complete multi-step tasks autonomously. We always implement human-in-the-loop checkpoints for irreversible actions, audit logging for every agent decision, and cost caps that pause agent execution if token usage exceeds a threshold. 2025 is genuinely the year of agents - 85% of organisations have integrated them into at least one workflow.