AI Integration Services
Embed AI into your existing stack.
Connect LLMs, vector stores, and AI APIs into your CRM, ERP, helpdesk, or custom application without rebuilding your architecture — we wire the intelligence in where it creates the most value.
Add AI where your system needs it — not where it's trendy
Most businesses don't need to replace their existing software to benefit from AI. What they need is AI capability wired into the systems they already run — a RAG-powered knowledge base inside their helpdesk, LLM-generated summaries in their CRM, or a classification model feeding their ERP. The value is in the integration, not the AI model on its own.
We specialise in API-first AI integration — connecting LLMs, vector stores, and AI inference services into your existing applications through well-designed middleware. We choose the integration point that creates the most business value, design the data flow, and build the connections that make AI a native part of your workflow — without requiring your software vendors to natively support AI features.
Business challenges we solve
The integration gaps that keep businesses from extracting value from AI.
AI Pilots That Never Make It Into Real Software
Proof-of-concept AI projects work in isolation but stall when it's time to connect them into actual business applications.
AI Vendors Without Native Integration Into Your Stack
The AI tool you want to use doesn't natively connect to your CRM, ERP, or custom application — leaving a manual gap.
Hallucinating AI That Doesn't Know Your Business
General-purpose LLMs give plausible but wrong answers because they don't have access to your actual data and documentation.
High LLM Costs from Unoptimised Prompting
Naive LLM integrations send far more tokens than needed, inflating API costs without improving output quality.
AI Features That Require a Platform Replacement
Being told you need to switch platforms to access AI features — when what you actually need is a well-designed integration layer.
Security Concerns About Sending Business Data to AI APIs
Legitimate concerns about PII, proprietary data, and compliance when routing business content through external AI APIs.
Our approach
How we design and build AI integrations that work reliably in production.
Integration Point Selection First
We identify exactly where in your existing workflow AI adds genuine value — not the technically simplest point, the most impactful one.
RAG for Business-Context Accuracy
When accuracy on your specific business data matters, we build retrieval-augmented generation pipelines rather than relying on base model knowledge.
Prompt Engineering for Production
We design prompt architectures for consistency, token efficiency, and predictable output formats — not just demonstrations.
Security-First Data Handling
PII masking, data minimisation, and compliant API routing are core design requirements — not afterthoughts.
Cost Monitoring & Optimisation
We instrument LLM API usage from the start and design prompts and caching strategies to control cost at scale.
Fallback & Error Handling Design
AI APIs are external dependencies that can fail or return unexpected outputs. We design graceful fallbacks for every AI call.
Key capabilities
LLM API Integration
Connect OpenAI, Anthropic, Google Gemini, or open-source models into your application through a managed API layer.
RAG Pipeline Construction
Knowledge bases indexed with vector embeddings, retrieving relevant context for grounded, accurate AI responses.
Vector Database Setup & Management
Design, deployment, and ongoing management of vector stores (Pinecone, Weaviate, pgvector) for your knowledge retrieval needs.
Semantic Search Integration
Replace keyword search in your application with AI-powered semantic search that understands meaning and intent.
AI Summarisation & Classification
Embed summarisation, categorisation, or classification capabilities into your existing content or support workflows.
Multi-Model Routing & Fallback
Smart routing across multiple AI models — using cost-efficient models for simple tasks, powerful models for complex ones.
Service offerings
LLM Integration Into Existing Applications
Wire LLM capabilities (summarisation, generation, classification) into your existing web, mobile, or desktop application.
RAG Knowledge Base Build
Index your documentation, policies, or product data into a vector store and build a retrieval pipeline for accurate, grounded responses.
CRM & Helpdesk AI Augmentation
Add AI-generated summaries, suggested replies, and automated categorisation into Salesforce, Zendesk, Freshdesk, or custom CRMs.
Semantic Search Implementation
Replace keyword search in any application with vector-powered semantic search that understands user intent.
AI API Gateway & Cost Control Layer
A managed API layer handling routing, caching, rate limiting, cost monitoring, and fallback across multiple AI providers.
AI Integration Audit & Optimisation
Assessment of an existing AI integration that's underperforming — identifying accuracy, cost, and reliability improvements.
Technologies & tools we use
Development process
From integration design to a monitored, cost-controlled AI capability in production.
01. Integration Discovery
3–5 Days- Use case definition
- Existing system API audit
- Data flow mapping
- Security & compliance review
02. Architecture Design
3–5 Days- Integration point selection
- RAG pipeline design
- Prompt architecture
- Cost modelling
03. Integration Build
2–4 Weeks- API connector development
- RAG pipeline construction
- Vector DB setup & indexing
- Prompt engineering
04. Testing & Accuracy Validation
1 Week- Output accuracy testing
- Edge case handling
- Performance benchmarking
- Cost measurement
05. Production Deployment & Monitoring
3–5 Days- Deployment to production
- Cost dashboard setup
- Alerting configuration
- Caching layer
06. Optimisation & Evolution
Ongoing- Prompt refinement
- Cost optimisation
- Model upgrades
- Knowledge base updates
Architecture & solution overview
The layers of a well-designed AI integration system.
Application Layer
Application Layer
Your existing application — CRM, helpdesk, web app, mobile app — where the AI capability is surfaced to end users.
Your Existing StackAI Integration Middleware
AI Integration Middleware
The managed API layer that handles routing, authentication, prompt construction, and response parsing between your app and AI services.
API Gateway / Custom MiddlewareKnowledge Retrieval Layer
Knowledge Retrieval Layer
RAG pipeline that retrieves relevant context from your indexed knowledge base before passing it to the LLM.
Vector DB + RAG FrameworkAI Provider Layer
AI Provider Layer
The LLM or ML inference service called with the constructed prompt — OpenAI, Anthropic, Gemini, or a self-hosted model.
LLM APIObservability Layer
Observability Layer
Token usage, latency, cost metrics, and error rates — tracked in real time to maintain quality and control spend.
Cost & Performance MonitoringIndustry use cases
The AI integrations we've built into existing business applications.
Helpdesk RAG-Powered Knowledge Base
A RAG integration surfacing relevant knowledge base articles to support agents inside Zendesk in real time — reducing average handling time by 28%.
CRM AI Summary & Next-Action Engine
LLM integration generating deal summaries and recommended next actions for sales reps inside Salesforce — from call notes and email threads.
Legal Document Semantic Search
A semantic search layer over 40,000 historical contracts — replacing keyword search with intent-aware retrieval for a legal team's due diligence workflow.
Benefits & business outcomes
AI Value Without Platform Replacement
Add AI capabilities to systems your team already uses — with no vendor migration, no retraining, and no workflow disruption.
Accurate, Business-Grounded AI Output
RAG-powered integrations produce answers grounded in your actual data — not hallucinated responses from a general-purpose model.
Controlled Costs at Scale
Cost-optimised prompt design and API usage monitoring keep AI spend predictable as usage grows.
Why choose our team
API-First Integration Specialists
We know how to connect AI capabilities into any application through its APIs — without requiring changes to the underlying platform.
RAG Architecture Depth
Retrieval-augmented generation is a core competency — we build knowledge pipelines that are accurate, updateable, and cost-efficient.
Security-Conscious by Default
PII handling, data minimisation, and compliant API routing are core design requirements on every integration we build.
Engagement models
Fixed-Scope AI Integration
A specific AI capability integrated into a specific system, delivered at a clear price and timeline.
AI Integration Retainer
An ongoing team iterating on AI capabilities across multiple systems as your requirements evolve.
AI Integration Audit
Assessment of an existing AI integration — identifying accuracy, cost, and reliability improvements.
Project delivery timeline
Typical timelines by AI integration scope.
Single Feature AI Integration
3–4 WeeksOne AI capability — summarisation, classification, or semantic search — integrated into one system.
Multi-Feature AI Integration
5–8 WeeksMultiple AI capabilities across one or two systems, with shared RAG knowledge infrastructure.
Enterprise AI Integration Layer
8+ WeeksA managed AI integration middleware handling multiple systems with unified cost monitoring and governance.
Frequently asked questions
No. Our AI Integration Services are specifically designed to add AI capabilities to your existing applications without requiring platform replacement. We build an integration layer that connects AI APIs and services to your current systems through their existing APIs — whether that's Salesforce, Zendesk, a custom ERP, or a proprietary application.
Ready to wire AI into your existing applications?
Tell us about the system you want to augment with AI and we'll design an integration that adds genuine value without requiring a platform replacement.