Stop Repeating Yourself: AI Assistants that Remember Your Work
Glean
Dec 2, 2025
Onboard once. Move faster forever.
How much time do teams waste re‑explaining context to AI? The new generation of assistants can remember across conversations and connect to your company knowledge, so they answer with citations to your files and decisions. That’s the difference between a clever demo and dependable day‑to‑day impact.
Big idea: Treat AI like a colleague you onboard—sources, standards, and guardrails—not a one‑off search widget.
Why this matters now
In 2025, enterprise features matured:
Company knowledge in ChatGPT brings context from connected apps (Drive, SharePoint, GitHub, etc.) straight into answers with citations.
Project memory lets teams carry context across chats so you don’t repeat yourself.
Data residency controls allow eligible enterprise/edu/API customers to keep data at rest in‑region.
These shifts turn AI into a trustworthy partner for operations, delivery and engineering.
Knowledge management, finally useful
Fragmented knowledge slows decisions. When assistants index your sources and remember team context, people get grounded answers instantly: “What did we agree in last week’s project review?” → the assistant replies with two bullet points and links back to the minutes and budget sheet. No more rummaging across silos.
How it works, in brief
Connect priority repositories (SharePoint/Drive/Confluence/GitHub/CRM).
Enable retrieval so answers include citations and deep links.
Use Projects / memory to persist team glossaries, standards and preferences.
Apply data controls (permissions, retention, residency) from day one.
The power of customisation and memory
Modern models are both smarter and easier to shape. With GPT‑5.1, you get more natural conversations, stronger coding performance, and features like extended prompt caching that keep long‑running context hot—ideal for multi‑turn work and retrieval‑heavy chats. Pair that with assistants that remember preferences and key facts across sessions, and the repetition disappears.
When agents do the heavy lifting
It’s not just text. Engineering teams are adopting agentic coding: repository‑level context (e.g., CLAUDE.md) keeps standards in view while the assistant proposes patches, drafts PRs and links to prior solutions. The same pattern fits marketing, finance, and ops—multi‑step tasks executed with traceability.
What good looks like (three real wins)
Engineering velocity
The assistant pulls your coding standards and previous migrations, drafts a plan, and opens a PR with references.
Client delivery without rework
It drafts a status update that cites the signed SoW and acceptance criteria.
Ops that scales
Onboarding plans assemble from policy docs, LMS content, and facilities checklists—with links, owners, and dates.
From idea to impact: your 90‑day rollout
Weeks 1–3: Foundations
Curate the 20 must‑answer questions per team.
Connect sources; mirror SSO/SCIM permissions.
Create project‑level context packs (glossary, brand/coding standards).
Weeks 4–8: Pilot
Turn on citations; require a source for every fact.
Compare assistant vs human search on time‑to‑answer and accuracy.
Add missing repositories; refine memory/context.
Weeks 9–12: Scale
Automate governance (DLP, retention, residency).
Enable training on “how to ask” and “how to check”.
Publish a living playbook; review monthly.
The Bottom Line
Stop repeating yourself. Connect your knowledge, add persistent team context, and insist on citations. With that, assistants become reliable colleagues who save hours, accelerate execution, and reduce cognitive load—without exposing data beyond existing permissions.
FAQs
Do assistants really remember now?
Yes—project/team memory carries context across chats, and some assistants store structured preferences to personalise future answers. Configure retention and scope.
Is this safe for regulated teams?
Yes—start with least‑privilege access, citations, audit logs, and in‑region data storage where available. Treat memory and context like configuration you can review.
Do we need a data lake first?
No. Begin with connectors and retrieval; federate search before you consolidate.
What about engineering work?
Use repository‑level context (e.g., CLAUDE.md) and agentic workflows to keep standards applied and PRs traceable.
AI assistants that remember connect to your company knowledge and carry context across conversations, delivering answers with citations to your files. With project memory, retrieval, and data controls (permissions, retention, residency), teams stop repeating themselves and move faster—safely and consistently.
