We integrate large language models (ChatGPT, Claude, Gemini, Llama) and build custom AI pipelines — contextual responses, document intelligence, semantic search, AI-powered features and enterprise AI integrations. We build production AI, not demos.
Companies embedding AI today are creating compounding advantages in UX and operational efficiency. Waiting 12 months means rebuilding from behind — not getting ahead.
Putting a ChatGPT wrapper on your product is not AI integration. Production AI requires RAG, context management, hallucination mitigation, cost optimisation and security controls. That is engineering.
Enterprise buyers expect AI features. Consumer products with AI personalisation see 25-40% higher engagement. LLM integration is moving from differentiator to table stakes — fast.
We build RAG pipelines letting LLMs answer questions about your proprietary data accurately. Document ingestion, embedding generation, vector database setup (Pinecone, Weaviate, pgvector), retrieval optimisation and response generation. Your AI answers from your data.
We integrate OpenAI, Anthropic, Google Gemini and open-source LLMs into your web app, mobile app or internal tool — with context management, token optimisation, rate limiting, fallback handling and cost monitoring built in.
AI writing assistants, smart search, automated summarisation, intelligent form completion, AI recommendations and conversational interfaces — engineered to match your product UX, not bolted on.
Document processing, automated classification, contract analysis, knowledge bases and AI-assisted decision support — built to enterprise security standards with audit trails, access controls and compliance documentation.
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Contract analysis, clause extraction, regulatory document search and compliance checking — AI with the precision and auditability legal teams require.
Clinical document summarisation, patient intake automation and clinical decision support — with HIPAA-aware architecture and audit logging.
Financial document analysis, transaction categorisation and risk assessment — with the security and compliance standards financial services demand.
AI features embedded into SaaS platforms — writing assistants, smart search, automated insights and intelligent workflow suggestions.
AI-powered product recommendations, natural language search, automated product descriptions and intelligent customer service.
Internal knowledge base search, document intelligence, automated reporting and AI-assisted process automation.
See how we've helped startups and enterprises with ai & llm integration — delivering measurable outcomes.
From startups to enterprises — businesses in 15+ countries trust Fortmindz for ai & llm integration that delivers measurable outcomes.
At Fortmindz, our AI integration process is designed to build production AI — not demos. Every phase produces real technical decisions based on evidence, so the AI feature we deliver works reliably, costs predictably and improves over time.
We begin by understanding the specific user problem the AI feature needs to solve, the data available to power it, the output quality required and the failure modes that are unacceptable. Most AI projects fail because the use case was poorly defined, not because the technology was insufficient.
We evaluate whether the problem is genuinely well-suited to LLM-based AI — or whether a simpler approach (search, filtering, rule-based logic) would deliver better results at lower cost.
We assess the quality, quantity, structure and accessibility of the data that will power the AI feature — identifying gaps that need to be addressed before the integration can work reliably.
We define measurable success criteria before building anything — what does good output look like, how will we measure it, and what accuracy threshold is acceptable for this use case.
RAG lets an LLM answer questions using your proprietary data — documents, databases, knowledge bases — rather than just its training data. Without RAG, ChatGPT cannot answer questions about your company, products or customers. With RAG, it can — accurately, with citations and without hallucination.
GPT-4: best for complex reasoning and code generation. Claude: best for document analysis, long context and nuanced writing. Gemini: best for multimodal tasks and Google workspace integration. We evaluate your requirements and recommend the right model — or a combination with routing logic that uses the best model per task type.
We implement multiple strategies: RAG with source citations, confidence scoring, output validation layers, structured output schemas and human-in-the-loop gates for high-stakes decisions. We also implement evaluation pipelines measuring answer accuracy against ground truth before and after deployment.
With proper controls, yes. We implement data sanitisation before LLM calls, use API tiers with data-not-training agreements, configure private deployment options (Azure OpenAI, AWS Bedrock) for sensitive environments, and build access controls. For highly sensitive data we can deploy open-source LLMs on your own infrastructure.
A focused LLM feature integration (writing assistant or document summarisation) takes 3-6 weeks. A full RAG knowledge base with document ingestion takes 6-10 weeks. An enterprise AI platform with multiple models, access controls and audit logging takes 10-16 weeks.
Yes. Most LLM integrations are additive — we connect to your existing backend via APIs, add the AI pipeline and surface results in your existing frontend. We assess your current architecture in discovery and design an integration approach that minimises disruption.
Tell us what you need. We'll come back within 24 hours with a specific technical approach, honest timeline and zero-obligation proposal.
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“Essential Designs was able to create a cutting edge application that will save lives, they always say "Anything can be done" and are definitely able to deliver on that promise.”
“Essential Designs was able to create a cutting edge application that will save lives, they always say "Anything can be done" and are definitely able to deliver on that promise.”
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