AI Solutions

Enterprise AI for instant company document search

4 weeks delivery, unlimited users.

Book a free 30-minute diagnosticExplore packages

Fixed-scope AI packages, delivered in 4 weeks

Scope and timeline agreed before we start

Customer inquiry AI chatbot

Answers repeat customer inquiries around the clock and hands complex questions to staff with full conversation context. Connects smoothly to messaging channels and website widgets.

  • Trained on existing FAQs and support history
  • Source citations and automated staff handoff
  • Messaging channel and web widget integration

3 weeks

Book a chatbot diagnostic

Workflow automation AI agent

A one-week diagnostic picks the workflow to automate, then we build it at a fixed price. LLM agents connect to your internal systems (APIs/DBs): repeat work such as quoting, ordering and reconciliation is handled by AI, and only the cases that need a check go to your staff.

  • Workflow analysis and diagnostic report
  • Integration with ERP, spreadsheets, and databases
  • Instant alerts to staff on failures or exceptions

1-week diagnostic + 4–5 weeks build

Book an automation diagnostic

Ideal for companies facing these challenges

Integrabbit is built for companies whose data lives on local NAS drives, PCs, or groupware, cannot leave the internal network, or want to avoid recurring per-user monthly subscription fees.

  • HR and admin teams are overwhelmed answering the same internal questions every day
  • When the person in charge is away, no one can find policies or decision history
  • Employees are uploading confidential documents to external ChatGPT, raising data leak concerns
  • Client NDAs or strict internal security policies prevent adopting public-cloud AI
  • Critical company documents are scattered across NAS drives and personal PCs without central management

Package Inclusions & Exclusions

Included

  • Requirements analysis and scope definition
  • Document indexing and search accuracy optimization
  • User search interface and admin dashboard
  • One pre-agreed internal system integration
  • Operator and administrator training (1 session)
  • Quantitative verification and final acceptance report

Excluded (Billed Separately)

  • Infrastructure (server & cloud) fees (billed at cost)
  • Commercial LLM API usage fees (billed at cost)
  • Additional system integrations exceeding the agreed scope
  • Excessive manual data cleanup (e.g. low-quality scan OCR re-entry)

4 weeks Build Process

Week 1

Document collection and scope definition

We select target company documents, audit formats and volume, and calculate any required pre-processing and data cleaning effort.

Week 2

Vector indexing and search quality tuning

Documents are chunked to build a vector index, then fine-tuned against your team's real-world queries for search accuracy and answer quality.

Week 3

UI integration and access control setup

We connect the employee search interface and admin dashboard, applying document access permissions by department and role.

Week 4

Quantitative acceptance and handover

We quantitatively verify accuracy against 50 real-world business queries, deliver a formal acceptance report, and train your team.

Selected Track Record & Case Studies

National R&D Project Analysis Pipeline
2025.03Finance & PublicNew Build

National R&D Project Analysis Pipeline

Researchers had to file a request to the development team every time they wanted to change anything in the pipeline, from data preprocessing to model inference. Given that this was a government research project where research direction changed often, researchers needed to be able to adjust the configuration themselves, without going through the development team.

Key technical decision

Broke everything from preprocessing to inference into individual nodes, connected together

Preprocessing steps and model combinations needed differed by researcher, so a single fixed pipeline wasn't enough. Making each step an independent node that researchers could pick and connect as needed meant they could try new experiment configurations immediately, without a development team request.

PyTorchRAGVector DB
Read the full case

Integrabbit AI Implementation Principles

A systematic approach combining proven AI deployment experience with modern technologies

Security-First Architecture

You decide where your data is processed and stored—from isolated private clouds to on-premise installations on your own hardware.

Quantitative Quality Assurance

Every answer cites its source document so anyone can verify it on the spot, and accuracy is quantitatively tested against 50 real-world queries before handover.

Transparent Fixed Scope

We deliver in 4 weeks against a clearly agreed scope. Any extended requirements are quoted upfront as transparent options.

Adoption criteria

4 weeks · Fixed scope
Delivery Timeline
No user limit · One-time fee
Pricing Structure
You pick the environment
Security Control
Citations · Test report
Quality Assurance

Frequently Asked Questions

Does our company data leave the network?
No. You decide where data is processed and stored before kickoff. We either use an isolated dedicated cloud or, for strict security requirements, deploy open-weight LLMs directly on your on-premise servers. In either case, your data is never used to train AI models, and this guarantee is legally binding in our contract. (On-premise deployments are quoted after verifying your server infrastructure during the initial diagnostic.)
Can it integrate with our existing systems?
What are the ongoing maintenance costs after delivery?
What happens if the AI generates incorrect answers (hallucination)?
How does the deployment and contract process work?

Discover your optimal scope and quote in a free 30-minute diagnostic

We propose a phased AI adoption roadmap tailored to your business environment and budget.

Book a free 30-minute diagnostic