Holded OpenAI / Claude integration
AI assistant with full context of your Holded for your team.
Implementation time · 10-20 días laborables
The problem
The team wastes time every day asking accounting for basic data: the sales rep asks whether client X is up to date, the CEO requests an ad-hoc quarterly report, finance grills the CSM about unbilled hours. Holded has the data, but opening it and navigating for a single question takes 3-5 minutes each time. Multiplied by 20-50 queries a day in a mid-sized company, those are hours lost on questions that a well-connected AI assistant answers in seconds. And ad-hoc reports like 'give me a cashflow projection with these assumptions' are still manual admin work.
ProposalThe solution
An internal chatbot in Slack or Teams that answers your team with real Holded data: 'how much is client X billing this quarter?', 'which invoices are due this week?', 'what is the margin on product Y?', 'generate the report of outstanding payments for me'. It uses RAG (Retrieval Augmented Generation) over the Holded API plus an LLM (Claude Sonnet 4.6, OpenAI GPT-4o or whichever model you prefer). Data never goes to model training; it is contractually protected.
What we automate
- Conversational assistant in Slack or Teams connected to Holded
- Natural language queries: 'how much did client X bill in 2026?'
- Generation of ad-hoc reports in natural language
- Smart alerts: 'projected cashflow < 50k euros in 30 days' to the CEO
- Weekly executive summary generated and sent to the CEO
- Outlier analysis: 'clients with billing down > 30% YoY'
- Assistant for admin: 'create an invoice for client X with concept Y'
- GDPR compliance: traceability of every query
Real use cases
These are the profiles that most ask us for the Holded ↔ OpenAI / Claude integration and what they get in the end.
40-employee services company with many queries to admin
Before: Admin received 30-50 questions a day on Slack about client status.
After: The bot answers 80% of queries. Admin gets back 2-3 hours a day.
CEO with weekly ad-hoc reports
Before: The CFO spent 4 hours every Monday preparing the executive report.
After: Automatic executive summary every Monday at 9:00. The CFO just reviews it.
Sales team with no access to Holded
Before: Sales reps ask admin whether their client is up to date.
After: The rep asks the bot about their client. Answer in 5 seconds.
What changes exactly
Without the integration
- 30-50 questions a day to admin about Holded data
- Ad-hoc reports consume 4-8 hours a week of the CFO
- Sales reps with no access to the real client status
- Executive reporting always a week behind
With the integration
- The bot answers 80% of queries in seconds
- Executive reports generated automatically
- Sales reps with client status in real time
- Monday's executive summary generated on Sunday night
How we build it
RAG architecture: we index the relevant Holded data (invoices, contacts, products) in a vector store (Pinecone, Qdrant or pgvector). When the user asks, we retrieve the relevant context and pass it to the chosen LLM (Claude Sonnet 4.6 by default for the best price/quality balance, OpenAI GPT-4o if you prefer). Function calling for actions (create an invoice, mark as paid). Everything is audited: each query is logged with the user, the question, the retrieved context and the response. GDPR compliant: the data is not used for model training.
Frequently asked questions
Does my data go to OpenAI?
Only the data needed to answer the query is sent to the model (RAG), not the entire database. Enterprise models (OpenAI Enterprise, Claude via API) do NOT train on your data: it is contractually protected. If it worries you, we set up a version with a local LLM (Llama 3.1, Mistral) on your infrastructure.
Claude or GPT-4o?
We recommend Claude Sonnet 4.6 for its price/quality balance and better handling of complex instructions. GPT-4o also works well. If you want open-source on your own infrastructure, Llama 3.1 70B via Together or Replicate.
Can it run actions, not just read?
Yes, via function calling. For example: 'create an invoice for client X for 1200 euros, concept consulting'. Every action requires user confirmation before it runs. Actions are audited.
Does it work in Slack and Teams?
Yes, in both as an official bot. Also as a web interface if you prefer, accessible via SSO.
How much does it cost to run (LLM tokens)?
It depends on volume. A small company with 200 queries a day, 30-60 euros a month in tokens. A large company with 2000 a day, 200-500 euros a month. We tune it with prompt caching and a vector store to minimise cost.
How long does development take?
Between 10 and 20 working days for the full version. A working MVP in Slack in 5-7 days, then we iterate on real use cases.
Shall we talk about your OpenAI / Claude integration?
A 30-minute call, no strings. You leave with scope and a price.