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From Copilot to LibreOffice: Strategies for Replacing AI-Assisted SaaS Without Losing Productivity

ppromptly

2026-01-29

11 min read

Practical guide to replace Copilot-style assistants with LibreOffice + open LLMs—preserve workflows, macros, and governance while keeping productivity.

Stop letting embedded AI dictate your workflows: practical steps to migrate from Copilot-style assistants to LibreOffice and open LLMs

Hook: If your organization is tired of opaque, embedded AI assistants (think: Copilot) that change behavior, capture data unpredictably, or lock workflows into a vendor stack, you can move to a predictable, auditable, open-source stack—without losing productivity. This guide walks IT teams and developers through a practical, low-risk migration strategy from embedded AI SaaS to LibreOffice + LLM integrations that preserve workflows, templates, and governance.

Why organizations are migrating in 2026

By early 2026, enterprise priorities have shifted: stricter data residency rules, rising costs of per-seat AI SaaS plans, and a maturing open-source LLM ecosystem made self-hosted AI integrations viable. Many orgs that adopted Copilot-style assistants for inline drafting and summarization now cite these pain points:

Unclear data capture and telemetry from embedded AI features.
Vendor lock-in for formats, templates, and assistant behavior.
Difficulty applying centralized governance and versioning to prompts and assistant actions.
Escalating costs as AI usage scales across employees.

Goal: Replace the assistant without breaking downstream processes—retaining templates, macros, change-tracking, and the productivity gains teams expect.

High-level migration plan

Audit current assistant usage — which Copilot features are actively used? Summaries, inline suggestions, document generation, code help, meeting recaps? Catalog by team and criticality.
Export and inventory assets — templates, macros, saved prompts, notebooks, and document stores. Ensure you can export DOCX, templates, attachments, tracked changes, and metadata.
Map to LibreOffice + LLM patterns — determine where LibreOffice covers formatting and where LLMs handle assistant behaviors (summarization, drafting, transformation).
Pilot — choose one team and run a full migration: export, convert, integrate an LLM-powered assistant, and measure productivity and quality.
Rollout and govern — establish prompt registries, version control, testing, and monitoring to scale safely.

Quick takeaway

Splitting responsibilities—LibreOffice for document fidelity and local LLMs for assistant intelligence—gives you control, privacy, and long-term cost predictability.

Phase 1 — Audit: what to capture before you cut the cord

Start by collecting empirical usage data. Talk to power users and capture representative examples of the assistant output you rely on. Key artifacts to collect:

Templates (DOCX/DOTX) and styles.
Macros (VBA, Office Scripts) and automation flows.
Saved prompts or templates used with Copilot-style assistants.
Example documents with tracked changes, comments, and metadata.
Workflows that touch other systems (CRMs, ticketing, DMS).

Create a one-page migration risk profile per workflow listing fidelity requirements (layout, pagination), functional needs (tracked changes, macros), and privacy constraints.

Phase 2 — Export and compatibility: getting documents out intact

LibreOffice supports OpenDocument (ODF) natively. The migration’s success depends on how well you can convert assets and preserve metadata. Use an automated, repeatable export strategy.

Export checklist

Batch-export DOCX/DOTX to ODT using LibreOffice headless or unoconv.
Preserve tracked changes and comments—verify a sample set manually.
Extract and archive embedded objects (Excel sheets, embedded images) and fonts.
Export to PDF/A for long-term archival when exact formatting is required.
Capture document-level metadata (author, last modified, version IDs) into a sidecar JSON if needed for compliance.

Command-line conversion (practical)

Use LibreOffice’s headless mode for bulk conversions. Example (Linux):

#!/bin/bash
mkdir -p converted_odt
for f in /exports/*.docx; do
  libreoffice --headless --convert-to odt --outdir converted_odt "$f"
done

For PDF archiving:

libreoffice --headless --convert-to pdf --outdir pdf_archive converted_odt/*.odt

If you need to extract metadata and comments programmatically, use Python libraries like python-docx (for DOCX) or odfpy (for ODT) to write sidecar JSON files with structured metadata.

Phase 3 — Macros, automation, and templates

Macros are often the trickiest part. Microsoft Office macros use VBA; LibreOffice supports LibreOffice Basic and Python. You have three practical options:

Port macros — translate VBA to LibreOffice Basic or rewrite in Python. This is worth it for business-critical flows.
Externalize automation — move macros into a centralized automation service that manipulates documents via CLI or APIs. This reduces per-document complexity.
Retire or redesign — replace marginal macro features with simpler templates or editorial guidance supported by LLM helpers.

Porting tip: implement unit tests for macro behavior (input document -> output document) and version them in Git. That gives clear rollback capability.

Phase 4 — Replacing assistant behaviors with LLMs

Copilot-like assistants provide a range of capabilities: summarization, rewriting, style alignment, inline suggestions, and task automation. Recreate these by integrating LLMs where they belong—in middleware or client plugins—while keeping documents in LibreOffice.

Architectural patterns

Local inference: Run an open LLM on-prem (GGML, GPTQ quantized models, or commercial on-prem runtimes). Best for sensitive data and predictable costs.
Private-hosted APIs: Self-host LLM inference behind your VPC with a REST interface for editors and automation services.
Hybrid: Use small local models for PII-sensitive prompts and cloud models for heavy-lift tasks, with strict data filtering.

2026 trend: composable assistant stacks

In late 2025 and early 2026, teams standardized on composable stacks: LibreOffice for editing; a small local LLM for sensitive text transforms; a vector store (Weaviate, Milvus) for context; and an API gateway that performs prompt governance, caching, and auditing.

Example: a lightweight assistant that summarizes a document

Architecture: LibreOffice extension sends document text to a local microservice -> microservice queries a local LLM -> returns a summary inserted as a comment or new document. Here's a minimal Python example using a hypothetical local REST LLM endpoint:

import requests

def summarize_text(text):
    payload = {
        "model": "local-llm-1",
        "prompt": f"Summarize the following document in 5 bullet points:\n\n{text}",
        "max_tokens": 300
    }
    r = requests.post("http://localhost:8000/generate", json=payload)
    return r.json().get('text')

# Usage: call from a LibreOffice extension that sends selected text

Replace the endpoint with your LLM runtime (Ollama, vLLM, or an inference server). Add authentication, rate limiting, and logging for governance.

Prompt management, testing and governance (PromptOps)

One of the most important lessons from 2025–2026: prompts become first-class assets. You need versioning, tests, and access controls.

Prompt registry basics

Store prompts as templated files in Git (YAML or JSON) with semantic versioning.
Write unit tests that assert expected outputs for canonical inputs.
Tag prompts with sensitivity levels (PII, public, internal) to control where they can run.
Integrate prompt linting to catch risky instructions and data exfiltration patterns.

Sample prompt file (YAML):

id: doc_summary_v1
name: Document summary (5 bullets)
model: local-llm-1
template: |
  You are a document summarizer. Generate five concise bullets capturing the main points.
  {{document_text}}
sensitivity: internal
version: 1.0.0

Testing example

Create a small test harness that runs the prompt against expected inputs and checks for hallucinations and style drift. Use integration tests in CI to prevent regressions when changing prompts or models.

Auditability, logging and compliance

You must capture logs for each assistant action. At minimum, log:

Prompt ID and version
Model used and model version/hash
Input context hash (not raw PII where policy forbids)
Output hash and a pointer to the full output (if needed for audits)
Timestamp and user identity

Store logs in an immutable store (append-only) and integrate with your SIEM or DLP for alerts on risky behavior. For legal implications around caching and residency see Legal & Privacy Implications for Cloud Caching in 2026.

Integrating with LibreOffice: extensions and middleware

Options to surface assistant features inside LibreOffice:

LibreOffice extension: Build an extension (Java/Python) that calls your LLM microservice and inserts results into the document as comments, suggestions, or new content.
Command-line hooks: Use headless LibreOffice + microservices to apply batch transformations (e.g., standardize language across a document corpus).
Collabora Online / CODE: If you want browser-based collaborative editing with LibreOffice fidelity, deploy Collabora Online and integrate middleware to provide assistant buttons in the UI.

Practical example: a simple LibreOffice-to-LLM roundtrip (pseudo-workflow)

User selects text in LibreOffice and clicks “Summarize” extension button.
Extension extracts text and sends to the local LLM microservice with a prompt ID.
Microservice enforces prompt policy, calls the model, logs the call, and returns generated text.
Extension inserts generated summary as a comment and attaches metadata (prompt ID, model version) to the comment.

Preserving collaborative features: tracked changes and review workflows

LibreOffice supports tracked changes and comments. To emulate the Copilot review experience:

When assistant suggests edits, insert them as proposed edits (comments with suggested replacement) instead of silently changing the text.
Store the assistant’s rationale in the comment body to help reviewers accept/reject confidently.
Keep the original text in the sidecar metadata so you can audit assistant edits.

Pro tip: Always surface the prompt ID and model version inside the suggestion to make assistant provenance visible to reviewers.

Data residency, PII and model selection

Choose the model and deployment strategy that matches sensitivity. If your policy forbids sending PII to external clouds, use an on-prem or private-cloud model. 2025–26 saw many organizations adopting small, audited models for sensitive transformations and using larger clouds only for non-sensitive tasks.

Quantifying productivity and validating quality

Set objective metrics for the pilot:

Time-to-first-draft (pre- and post-migration)
Review cycles per document
Formatting regressions (manual fixes required after conversion)
User satisfaction via quick NPS surveys

Run A/B tests: let one group keep the old assistant while the other uses LibreOffice + LLM middleware. Measure change acceptance rates and editorial time saved. For analytics and measurement guidance, see the Analytics Playbook for Data-Informed Departments.

Rollback and continuity planning

Always keep a fallback. Exported DOCX/PDF archives and an emergency short-term Copilot license can be part of a rollback plan. Maintain the original documents and metadata for at least the duration of the pilot plus one audit window. If you’re planning large infrastructure moves, the Multi-Cloud Migration Playbook has useful recovery risk patterns.

Cost model and TCO

Moving off per-seat Copilot can reduce subscription costs but introduces infrastructure and MLOps costs. Key components to model:

Infrastructure for LLM inference (GPUs, servers) or costs for private-hosted inference.
Engineering time to build extensions and prompt registries.
Operational costs for governance, monitoring, and model updates.

Tip: Start with a small, cost-effective local model for sensitive workloads and evaluate incremental scaling only when the pilot proves productivity improvements.

Developer playbook: concrete steps and snippets

Provision a local LLM inference service (example: Ollama, vLLM, or a managed private inference service).
Implement a prompt registry—a Git repo with templated prompts and tests.
Build a minimal LibreOffice extension that calls your service. Use LibreOffice Python scripting for rapid iteration.
Write CI tests for prompt outputs and macro behavior.

LibreOffice extension skeleton (pseudo):

# Python pseudo-code run in LibreOffice's scripting environment
from com.sun.star.task import XJobExecutor
import requests

class SummarizeExecutor(XJobExecutor):
    def trigger(self, args):
        doc = XSCRIPTCONTEXT.getDocument()
        selection = doc.getCurrentSelection()
        text = selection.getString()
        r = requests.post("http://localhost:8000/generate", json={"prompt_id":"doc_summary_v1","text":text})
        summary = r.json()['text']
        # Insert as comment
        cursor = doc.Text.createTextCursor()
        doc.createInstance("com.sun.star.text.TextField.Comment")
        # ... attach summary and metadata

Case study: 6-week pilot outline (example)

Week 1: Audit and export (collect templates, macros, examples)
Week 2: Convert and validate (format checks, tracked changes)
Week 3: Build minimal LLM microservice and prompt registry
Week 4: LibreOffice extension + user testing
Week 5: Measure, fix defects, and iterate on prompts
Week 6: Rollout to pilot team and evaluate metrics

Common pitfalls and how to avoid them

Underestimating macros: Treat them as code—test and version them.
Ignoring prompt provenance: Without prompt IDs and model versions you can’t audit outputs.
Skipping user training: Users must learn the new “review” flow—assistant suggestions should be proposals, not automatic edits.
Overcomplicating the stack: Keep the initial design minimal: LibreOffice + one local LLM + Git prompt registry.

Future-proofing: next steps for 2026 and beyond

Expect these trends to guide future work:

Standardized prompt metadata and registries will become common enterprise controls.
Model provenance (hashes, audit logs) will be required for regulated sectors.
Edge inference and tiny models will handle most sensitive tasks locally while larger models operate in restricted cloud zones.
Open-source toolchains for prompt testing, diffing assistant outputs, and automating document regression tests will mature quickly in 2026.

Final checklist before you flip the switch

All critical docs exported and verified for formatting and tracked changes.
Macro inventory completed and either ported, externalized, or deprecated.
Prompt registry in Git with tests and versioning enabled.
LLM inference endpoint deployed with logging, auth, and rate limits.
LibreOffice extension or middleware validated with user acceptance tests.
Rollback plan documented and a short-term fallback available.

Conclusion and action plan

Replacing an embedded AI assistant like Copilot doesn't mean sacrificing productivity. By separating concerns—LibreOffice for document fidelity and open LLMs for assistant intelligence—you gain control, reduce vendor lock-in, and meet compliance needs while keeping users productive.

Actionable next steps (pick one today):

Run a 2-week audit: export 50 representative documents and validate conversions.
Spin up a local LLM inference endpoint and test three sample prompts against real documents.
Build the simplest LibreOffice extension that posts selected text to your LLM and returns a comment.

We’re in a moment (late 2025 → 2026) when open-source LLMs and LibreOffice together can deliver a secure, auditable, and cost-effective assistant experience. With a structured migration plan you can ditch opaque, embedded AI assistants and keep—or even improve—the workflows people rely on today.

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