Put AI to work inside your business.
We build private assistants, knowledge bases and workflow automations on your own data, securely connected to the tools your team already uses. Less searching, less copy-and-paste, more time on the work that matters.
AI that knows your business
Generic AI tools know the internet. Internal AI infrastructure knows your documents, your customers and your processes, and works inside your systems.
Every team has the same hidden cost: people searching for the latest version of a document, asking a colleague the same question again, or copying information from one system into another by hand.
Internal AI infrastructure connects AI to the knowledge and tools you already have. Your team asks a question in plain language and gets an answer drawn from your own material, with the source linked. Repetitive multi-step tasks run as automations, with a person approving anything that matters.
It is built for your business specifically: your data sources, your permissions, your workflows. It is not another app to log into; it shows up where your team already works.
From scattered to workingFig. 01
Drives, wikis, inboxes, CRM notes and people's heads.
Your sources indexed, with existing permissions respected.
Grounded in your material, with the source linked.
Multi-step tasks handled, with approvals where needed.
Less searching and re-keying, more time on real work.
Three building blocks
Most projects start with one of these and grow into all three, because they share the same secure foundation.
Private assistants
- Chat assistants for specific teams or roles
- Available in Slack, Teams, the browser or your own tools
- Draft replies, summaries, proposals and reports
- Only see what each user is allowed to see
Knowledge bases
- Your documents, policies and records made searchable
- Questions answered in plain language
- Every answer linked to its source
- Kept in sync as your material changes
Workflow automations
- Multi-step tasks across your tools
- Read, classify, extract, draft and update
- Human approval on anything that matters
- Logged, monitored and easy to switch off
The architecture, in plain words
Four layers. Your data stays where it lives; AI is given controlled, permissioned access to it.
Internal AI architectureFig. 02
Documents, wikis, email, CRM, helpdesk, databases and spreadsheets.
Content is organised for search, with each user's existing access rights applied.
A language model answers from retrieved content and uses approved tools.
Slack, Teams, your CRM, browser or an internal portal.
- 01
Connectors
We connect to your sources through their official integrations and APIs, and keep the index in sync as content changes.
- 02
Retrieval, not guesswork
When someone asks a question, the system first finds the relevant passages from your material, then the model answers from those, and shows where they came from.
- 03
Permissions carried through
If someone cannot open a file today, the assistant will not use it to answer them. Access follows your existing rules.
- 04
Defined actions only
Automations can only use the specific tools and steps we give them, and anything sensitive waits for a person to approve it.
What your team actually sees
Two everyday examples: a question answered from internal knowledge, and a routine task handled by an automation.
Internal assistant · IllustrativeFig. 03
- Account manager
"What did we agree with Harbour Logistics on payment terms, and is that within our standard policy?"
- Assistant · searching
Contract folder, CRM deal notes, finance policy. Only sources this user can access.
- Assistant
"The signed agreement sets 45-day payment terms. The standard policy is 30 days; terms over 30 need finance director approval, which is recorded in the deal notes."
- Sources3 documents linked
Master services agreement · Finance policy · CRM deal note. One click to check each one.
Fictional company and details, shown for illustration only.
Workflow automation · IllustrativeFig. 04
- TriggerNew supplier invoice arrives
Sent to the accounts inbox as a PDF.
- ExtractKey details read
Supplier, amount, due date and purchase order number.
- CheckMatched against the order
Differences flagged instead of passed through.
- ApproveA person signs it off
One-click approval in your team's chat.
- OutcomeEntered and logged
Draft bill created in the accounting tool, with a full audit trail.
Where it earns its keep
The best first projects are frequent, repetitive and text-heavy. Here is where we usually find them.
Sales
- Account research and call preparation
- Proposal first drafts from past wins
- Meeting notes written back to the CRM
Customer support
- Suggested replies grounded in your help content
- Ticket triage, tagging and routing
- Gaps in documentation surfaced
Operations
- Process and SOP questions answered instantly
- Data moved between systems without re-keying
- Status reports compiled automatically
Finance
- Invoice and receipt data extraction
- Policy and contract term lookups
- Month-end commentary drafted for review
People & HR
- Policy and benefits questions answered
- Onboarding assistant for new starters
- Job descriptions and documents drafted
Marketing
- On-brand drafts using your guidelines
- Research summaries and content briefs
- Campaign results pulled into one report
Private by design
Putting company data near AI is a reasonable worry. We treat it as the first design decision, not an afterthought.
- 01
Data kept within systems you control
We build on your accounts and infrastructure where possible, so your data, index and logs stay in your hands.
- 02
No training on your data
We use model providers and settings under which your content is not used to train their models, and confirm the terms with you.
- 03
Access controls and audit logs
Who can use which assistant, what it can see, and a record of questions, answers and actions taken.
- 04
Least access, human approval
Each assistant or automation gets only the access it needs. Sending, paying, deleting or anything customer-facing can require sign-off.
- 05
Honest about limits
AI can be wrong. That is why answers cite sources, uncertain cases are flagged, and people stay in charge of decisions.
Agreed with you before we buildFig. 05
- Which data sources are in scope, and which are off limits
- Who can use each assistant, and what each person can see
- Where data, indexes and logs are stored
- Which AI models and providers are used, and on what terms
- How long conversations and logs are kept
- Which actions need human approval
- How to pause or switch off any automation
Your own security and compliance requirements come first. We design to them and document what we build.
Built into your existing stack
We connect to the tools your team already uses, so adoption does not depend on anyone learning a new system.
Chat & email
- Slack and Microsoft Teams
- Email inboxes
- Internal portals
Documents
- Google Drive and SharePoint
- Notion and Confluence
- PDFs, contracts and policies
Business systems
- CRM and helpdesk
- Accounting and ERP
- Project management
Data & APIs
- Databases and warehouses
- Spreadsheets
- Most tools with an API
Examples, not a fixed list. We confirm exactly what connects, and how, during discovery, before anything is built.
Not another chatbot subscription
How purpose-built internal AI differs from giving everyone a general AI chat tool and hoping for the best.
| General AI chat tools | Growth Loop internal AI | |
|---|---|---|
| Knowledge | Whatever people paste in each time | Connected to your sources and kept in sync |
| Answers | Hard to verify, no link to your documents | Grounded in your material, with sources shown |
| Permissions | No awareness of who should see what | Each user's existing access rights applied |
| Actions | Copy and paste the output into other tools | Defined actions inside your systems, with approvals |
| Data handling | Depends on each person's account and settings | Agreed centrally, with access controls and audit logs |
| Adoption | Left to individuals | Designed around specific roles and workflows |
| Improvement | None beyond vendor updates | Reviewed and refined based on real usage |
Start small, prove it, extend
Five stages, starting with one high-value use case. Timelines depend on scope and systems, and are agreed with you on the first call.
- 01
Discovery
We map where time goes, which knowledge people hunt for, and which systems hold it. Together we pick the first use case.
- 02
Design & knowledge
We agree data sources, permissions, approval points and what good answers look like, and prepare the content.
- 03
Build & integrate
We build the knowledge layer, assistant or automation and connect it to your tools, inside your environment where possible.
- 04
Test & launch
A pilot group tests it against real questions and tasks. We fix gaps, then roll out with short training.
- 05
Monitor & improve
We review usage, answer quality and feedback, refine the system and add the next use case.
What we measureFig. 06
Illustrative only. We agree the measures with you up front and report your real numbers alongside user feedback.
Best fit for
Any team that runs on documents, repeat questions and manual hand-offs between tools. These are the teams we build it for most.
Thought Leaders & Lean Teams
- An assistant trained on your talks, writing and frameworks
- Proposals and follow-ups drafted in your voice
- Admin automated so a small team stays small
B2B Teams
- Sales and support answers from one source of truth
- CRM kept up to date without manual entry
- Faster onboarding for new hires
Ecommerce Brands
- Product and policy knowledge for your support team
- Order, returns and supplier admin automated
- Reporting pulled together from every channel
Investors
- Searchable knowledge across memos, decks and notes
- First-pass summaries of inbound pitch materials
- Portfolio updates compiled into one view
Internal AI questions we get most
Still unsure? Book a call and we'll answer them for your business specifically.
Is our data safe? Will it be used to train AI models?
- We build within systems you control wherever possible, and agree where data, indexes and logs are stored before we start.
- We use model providers and settings under which your content is not used for training, and share the terms with you.
- Access controls and audit logs are part of the build, not an optional extra.
How is this different from just using ChatGPT or Copilot?
General tools are useful, but they do not know your business, cannot see your systems in a controlled way and leave each person to manage their own data. We build the connections, permissions and workflows that let AI work on your own knowledge and inside your tools. Where you already use one of these products, we can often build on it rather than replace it.
What if the AI gets something wrong?
It can, which is why we design for it. Answers are grounded in your documents and link to their sources so people can check them. The assistant is set up to say when it cannot find an answer rather than guess. Automations that send, pay or change records can require a person to approve them.
Will it see things people shouldn't?
No. Where your sources support it, each user's existing permissions are carried through, so the assistant only uses material that person can already open. Sensitive sources can be excluded entirely or given their own restricted assistant.
How long does it take?
It depends on the use case, the number of data sources and how clean they are. A focused assistant on a well-organised set of documents is much quicker than an automation across several systems. We start with one use case and scope a realistic timeline with you on the first call.
What do you need from us?
A sponsor who owns the outcome, time with the people who do the work today, admin access (or someone who has it) for the systems in scope, and your security requirements. During the pilot, a small group willing to test it and tell us honestly what works.
Who maintains it after launch?
Your choice. We can keep monitoring and improving it, or hand it over with documentation so your team can run it. Either way, it is built on your accounts, so you are never locked out of your own system.
How do you measure success?
Against measures agreed at the start: how many people use it, how often answers are rated helpful, how many tasks run automatically, and time returned to the team. We report real usage, not just what was built, and use it to decide what to improve next.
AI agents that answer, qualify, book and follow up around the clock.
The Growth Loop CycleSmarter every cycleHow we design, build, measure and improve every system we run.
Outbound SystemsGo find the people who need youPersonalised, multi-channel outreach logged in your CRM.