What we integrate
Codse Tech integrates AI directly into existing software stacks so teams can ship value without rewriting the product.
- Retrieval-Augmented Generation (RAG) pipelines tied to product docs and internal knowledge
- AI agents for multistep workflows and operations
- Structured LLM outputs that write safely into existing systems
- Domain chatbots for support and internal enablement
- Document intelligence for extraction, classification, and summarization
How it works
Discovery (1 week)
Audit workflows, data sources, and success metrics. Define measurable outcomes before implementation.
Sprint (2-4 weeks)
Build integrations, retrieval, and tool-use flows with guardrails and quality checks.
Ship
Roll out with observability, fallback paths, and clear escalation mechanisms.
Retainer
Extend capabilities, tune performance, and support production operations month-over-month.
Tech stack
- Models: Claude API, AWS Bedrock, OpenAI
- Orchestration: LangChain and custom execution layers
- Data: vector databases, Postgres, analytics stores
- Runtime: secure APIs, queues, and monitoring pipelines
Pricing framework
| Engagement | Typical range | Timeline |
|---|---|---|
| Discovery sprint | $2K-$5K | 1 week |
| Integration sprint | $10K-$30K | 2-4 weeks |
| Production rollout | $25K-$75K | 4-8 weeks |
| Ongoing retainer | $5K-$15K/mo | monthly |
FAQ
What are AI integration services?+
AI integration services embed AI capabilities into existing products and operations using secure tool access, retrieval, and measured delivery.
How much does AI integration cost in 2026?+
Most projects begin with a fixed discovery sprint and continue into delivery sprints. Cost depends on data complexity, systems involved, and compliance requirements.
How long does it take to integrate AI into an existing product?+
A first production version is commonly delivered in 2-6 weeks when access to systems and datasets is ready.
What's the difference between AI integration and building from scratch?+
Integration extends existing products and workflows. Building from scratch replaces major systems and usually requires longer timelines and larger budgets.
How do you handle data privacy when integrating AI?+
Access is scoped by least privilege, sensitive data paths are controlled, and all major workflows include logging, evaluation, and fallback behavior.
Internal links and next steps
- Start from the full Services overview
- Read the MCP primer: What is MCP server in 2026
- Read the agent delivery guide: AI agent development framework