CodesSavvy is a senior engineering studio offering AI integration services. We embed OpenAI, Anthropic Claude, and Gemini into existing SaaS products, dashboards, and customer portals. RAG, semantic search, copilots, document intelligence. Production-ready in 6 to 10 weeks. Fixed price. We work with startups and businesses in the US, UK, Canada, and Australia.
AI Integration Services
for Software That Already Ships
We add AI features to your existing software without rebuilding it. OpenAI, Claude, or Gemini integration. RAG with vector databases. Semantic search. Copilots and document intelligence — all wired into the product you already have.
The Honest Part
Should You Actually Add AI?
Most AI integration agencies will quote you regardless. We will not. Half of the AI features clients ask for would work better as a regex, a SQL query, or a deterministic workflow. Here is our honest decision matrix.
AI Actually Helps When
- ✓Users ask natural-language questions that map to your unstructured content (docs, tickets, transcripts)
- ✓You need to summarize long content (meeting notes, customer feedback, support threads)
- ✓You classify free-form input into categories that change often (sentiment, intent, topic)
- ✓You extract structured data from messy sources (PDFs, emails, scanned documents)
- ✓You generate first drafts users will edit (proposals, emails, reports, descriptions)
- ✓Users need to find things by meaning, not exact keywords (semantic search)
AI Is Expensive Theatre When
- ✗A deterministic rule, regex, or SQL query would do the job 100x cheaper and more reliably
- ✗You need exact, repeatable answers every time (calculations, lookups, validations)
- ✗The output has to be auditable or compliant — LLMs can hallucinate at the worst moment
- ✗You are adding it because investors want AI on the pitch deck, not because users will use it
- ✗The same task can be solved with a properly designed form or workflow
- ✗You have not validated that users actually want a chatbot — most do not
Our rule: if a feature would work as a deterministic system, build it deterministically. Save AI for the work that genuinely needs language understanding, reasoning, or pattern recognition over unstructured data. This is the difference between AI integration services that ship results and AI integration services that ship API bills.
What We Integrate
We work across the major language model providers and the open infrastructure that surrounds them. The integration choice depends on your use case, not on which provider has the loudest marketing.
Large Language Models
OpenAI GPT-4 and GPT-4o, Anthropic Claude 3.5 Sonnet and Opus, Google Gemini 1.5 Pro and 2.0. We benchmark all three on your specific task and pick the right one — often two, with failover.
Vector Databases & RAG
pgvector on Postgres for most cases. Pinecone for high-scale. Weaviate or Qdrant for self-hosted requirements. Embedding pipelines with OpenAI text-embedding-3 or Cohere v3.
Semantic Search
Replace keyword search with semantic understanding. Users find things by meaning. Works across docs, support tickets, product catalogs, customer records — anywhere search is text-heavy.
Document Intelligence
PDF extraction, structured data parsing from invoices and contracts, OCR-to-structured pipelines, contract clause analysis. Built on the model best suited to the document type.
Copilots Inside Your Product
Embedded AI assistants that know your data — not generic chatbots. Function calling lets the copilot read and act on real records inside your app, not just answer in text.
Streaming & Function Calling
Token-streamed responses for snappy UX. Function calling for AI that can read your database, call your APIs, and trigger workflows. Tool use orchestration for multi-step tasks.
Patterns We Ship Most
5 AI Integration Patterns That Actually Work
Semantic Search Over Your Content
3-4 weeksWhen to use: Users find docs, tickets, or records by what they mean — not exact keywords.
Stack: pgvector + OpenAI embeddings + your existing app DB
Document Intelligence Pipeline
4-6 weeksWhen to use: Extract structured data from PDFs, invoices, contracts, or unstructured uploads.
Stack: GPT-4o or Claude 3.5 + function calling + validation layer
RAG-Powered Knowledge Assistant
5-7 weeksWhen to use: An assistant that answers questions using your private content — docs, wiki, support history.
Stack: Embeddings + vector store + LLM + citation layer
AI Drafting Inside Your UI
3-5 weeksWhen to use: Users write proposals, emails, reports faster with AI-generated first drafts they edit.
Stack: Streaming LLM + structured prompts + edit history
Smart Classification & Routing
2-4 weeksWhen to use: Categorize incoming tickets, leads, or content into actionable buckets.
Stack: Lightweight LLM + structured output + fallback rules
Real Cost Breakdown
What AI Integration Actually Costs in 2026
Two cost lines you need to budget for: the integration project itself, and the ongoing API costs at your usage tier. Here is the honest math.
Integration Project Cost (Fixed Price)
Ongoing LLM API Costs (Monthly)
Cost projection is part of every engagement. Before any code is written, you receive a written cost model with usage assumptions, per-user API cost, and a 12-month projection at three growth scenarios. No AI integration agency that hides ongoing costs is worth working with — they are setting you up for a six-figure API bill at scale.
Our Process
The 4-Week AI Integration Sprint
A fixed-scope, fixed-price format for adding one AI feature to your product. Most engagements run two or three sprints. You sign off after each before the next begins.
Scope, Provider Benchmark & Cost Model
We map the feature to the right pattern from our matrix. We benchmark OpenAI, Claude, and Gemini on your real data. You receive a written cost projection with growth scenarios — before any code is written.
Prototype on Production-Like Data
We build a working prototype against a slice of your real data. You test it. We measure latency, accuracy, cost per call. Adjustments happen here, before integration begins.
Production Integration
Wire the feature into your live product. Function calling, streaming, error handling, rate limiting, fallback logic. Deployed to staging, behind a feature flag, fully testable.
Cost Optimization, Observability & Handoff
Prompt caching, model routing, response caching where safe. Token usage dashboards. Cost alerts. Full documentation. The team that builds it shows your engineers how to maintain it.
AI Integration — Frequently Asked Questions
Get a Free AI Integration Roadmap
Send us what you have built and what AI feature you are considering. We send back a written roadmap with the right pattern, the right provider, a cost model, and a realistic timeline. No sales call required.
Request Your Free Roadmap