RAG & Agentic AI
MVP Builds
Your AI MVP should answer from your data, not guesses. We build internal knowledge-base chatbots, RAG systems, and small agentic workflows that retrieve from your documents, databases, or approved sources.
This service is for founders, product teams, and internal teams that need a working prototype with clear limits. Each build has a fixed scope, timeline, and handoff plan before implementation starts.
What We Build
RAG systems with clear boundaries.
We define data sources, retrieval behavior, evaluation checks, and deployment target upfront. The goal is a usable AI MVP you can test with real users or internal teams.
Internal Knowledge-Base Chatbot
Your team asks questions in natural language and the system retrieves answers from internal docs, wikis, or databases. We can deliver a web UI, Slack bot, Teams bot, or API depending on the fixed scope.
Privacy-First / On-Prem RAG
For use cases where data cannot leave your network. We can scope local embeddings, vector stores, and self-hosted or approved model access based on your security and data residency requirements.
Agentic Workflows
Small multi-step AI workflows for tasks such as document lookup, summarization, report drafting, and tool calling with logged actions.
Document Ingestion
Ingestion pipelines that parse, chunk, embed, and index your content for retrieval. Supports PDFs, docs, spreadsheets, web pages, and databases.
Platform Integration
Slack bots, Teams integrations, web apps, and API endpoints so the assistant fits into the workflow your team already uses.
3wk
Average MVP delivery time
100%
Fixed-scope, fixed-price engagements
10+
RAG systems deployed in production
30d
Post-launch support included
Technology
Practical AI stack.
We use documented tools that are easy to inspect, deploy, and replace. No black boxes and no avoidable vendor lock-in.
Process
MVP build in weeks, not months.
Every engagement follows a tight, scoped workflow. We agree what the AI system should do, what it should refuse, and how success will be checked.
Requirements & Scoping
Share your use case, data sources, user roles, and success criteria. We define a fixed scope with deliverables, exclusions, and timeline.
Architecture & Prototype
We design the retrieval flow, model access, storage, and UI/API shape. You see a working prototype early, not just a diagram.
Build & Evaluate
We implement the MVP and test it with real questions, documents, and failure cases. Feedback is handled within the agreed scope.
Deploy & Handoff
Deployment goes to your chosen infrastructure where possible, with documentation, environment notes, and 30 days of post-launch support.
Need a scoped AI MVP
you can test?
Tell us about your use case. We respond within 24 hours with a fixed scope, timeline, and price if the project is a fit.
spacedrift.contact@gmail.com