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AI Integration Development — Add Intelligence to What You Already Built

We add AI features to existing products — chat assistants, document Q&A, smart search. No boilerplate demos, no AI washing. Working code in your repository.

Tell us your use caseSee Our Cases

Codevia integrates OpenAI, Gemini, and open-source LLMs into your existing web and mobile product. We do not consult on AI strategy — we write code. Our focus: ai integration development without rewriting your existing backend. RAG pipelines on your documentation, streaming chat responses, embedding-based search across large catalogs — all integrated into your product in 2–8 weeks.

What We Build

Chat Assistants

LLM-backed chat inside your application: customer support, onboarding guide, internal helpdesk. Streaming generation, conversation context, human escalation fallback.

Document Q&A (RAG)

Upload PDFs, Confluence, Notion, or database content — users ask questions in natural language and get answers with source citations. Vector search via pgvector or Pinecone.

Smart Autocomplete

AI suggestions in editors, forms, or search. Dramatically improves UX for products with manual text input — descriptions, reports, messages.

Content Generation Pipelines

Automated generation of descriptions, reports, emails, or specs from structured data. Reduces manual work inside your SaaS or internal tools.

LLM Features in Admin Panels

AI summaries in dashboards, auto-tagging, classification, anomaly detection. AI value where your team already spends time.

Who This Is For

SaaS Founders

You want an AI feature in your roadmap but are not ready to hire an AI specialist full-time. LLM integration service is our standard engagement.

Product Teams at Mid-Size Companies

Mature product with an established backend where AI can improve a key workflow — search, content, support.

Startups With an Existing Backend

Backend is already built but no AI expertise on the team. We add AI features for existing product without rebuilding your architecture.

How We Integrate

  1. 01

    Scope & model selection

    We review your use case, select the right LLM (OpenAI, Gemini, open-source), and define the integration surface — what data goes in, what the model returns, how you use the output.

  2. 02

    Prompt engineering & retrieval design

    We design prompts and, where needed, build a retrieval pipeline: chunking strategy, embedding model selection, vector store setup (pgvector, Pinecone, Weaviate).

  3. 03

    Integration & backend wiring

    We add the AI feature to your existing backend — API handlers, streaming endpoints, rate-limit management, cost tracking. No rewrite of your core product.

  4. 04

    Evaluation & quality pass

    We run a structured evaluation: edge case prompts, hallucination checks, latency benchmarks. We tune until output quality meets your acceptance criteria.

  5. 05

    Deploy & handover

    We deploy to your infrastructure and document the integration. The first 30 days of post-launch prompt tuning are included.

Technologies

  • OpenAI API
  • Anthropic Claude API
  • Google Gemini
  • LangChain
  • LlamaIndex
  • pgvector
  • Pinecone
  • Weaviate
  • .NET 8
  • Python
  • FastAPI
  • Next.js

Selected Cases

Bitzlings — AI Dev Team SaaS

Codevia built the full Bitzlings product — an AI-first SaaS with dev team tooling. Includes LLM-powered features, workflow automation, and integrations. Three weeks from Figma to MVP launch.

Read the case study

Timelines and Pricing

Honest minimum budgets. No 'contact us for a quote' games.

Project typeTimelineBudget
Simple chatbot (FAQ, menu, scripted flows)2–3 weeksfrom $1,500
RAG on your documents (vector search + LLM)3–5 weeksfrom $3,000
Full LLM feature in existing product4–8 weeksfrom $5,000

More complex integrations (multiple LLMs, custom fine-tuning, high-load) are scoped individually.

Frequently Asked Questions

It depends on model and usage volume. GPT-4o runs around $5 per million input tokens. For most business applications — a chat assistant or document Q&A with moderate traffic — monthly API costs land between $50 and $300. We help you design prompts and retrieval pipelines to minimize token consumption.
OpenAI's API terms state they do not use API data for model training by default. For stricter data requirements we can deploy open-source LLMs (Llama, Mistral) on your own infrastructure or use Azure OpenAI Service within your cloud tenant.
OpenAI and Gemini give you the best capability per dollar for most tasks. Open-source models (Llama 3, Mistral, Phi) make sense when data privacy rules out third-party APIs, or when you need to run inference at scale without per-token costs. We recommend based on your specific constraints.
We implement streaming responses so users see output as it generates — similar to ChatGPT. Latency is typically 200–800ms to first token. For latency-critical flows we use smaller, faster models or pre-computed embeddings.
We can set up a monthly maintenance retainer or hand off to your internal team with full documentation. LLM integrations require occasional prompt tuning as models update — we include the first 30 days of post-launch adjustments in every project.
Yes, that is the most common engagement. We audit your existing backend, identify the integration points, and add AI features without rewriting your core product. We have integrated into .NET, Node.js, Python, and Firebase backends.

Ready to add AI to your product?

Tell us what you want AI to do in your product — we will get back to you with a scoped estimate within one business day.

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