Every building company business we've worked with has a different definition of "broken". AI knowledge base only earns its keep when it solves the specific definition you'd give it on a bad day - which is why our first call is mostly listening.
Building companies-grade AI knowledge base, shipped by people who've done it before.
Our goal is to give building companies a three-day weekend, so people can spend more time with their families and the people they love :)
What AI knowledge base actually does
Your team's tribal knowledge, finally searchable. Upload your SOPs, training videos, past emails, and Slack threads - your team asks questions and gets answers with citations.
- 01 Ingests PDFs, Word docs, videos, Slack, Notion, Drive
- 02 Answers with citations back to source documents
- 03 Permission-aware - staff only see what they should
- 04 Detects stale docs and prompts owners to update
Built on: Claude Pinecone Vercel AI SDK Postgres MCP
Without Kiwi Dynamics
With Kiwi Dynamics
What you actually get
Every engagement is scoped and quoted up front. This is what is in the box.
- A working pilot, in productionNot a prototype on someone's laptop. The first slice of AI knowledge base runs against real work within weeks.
- Your data stays yoursIt runs on your accounts and your tools. If we part ways you keep the system and everything in it.
- The workflow mapped before codeWe write down what good looks like for building companies first, so nobody is guessing at handover.
- Support after it landsThe people who built it stay reachable when the business changes shape around it.
How AI knowledge base compares
The two things most businesses do instead, and where each one runs out.
| Hiring for it | An off-the-shelf tool | Kiwi Dynamics | |
|---|---|---|---|
| Fit to how you work | Fits perfectly, costs a salary | You bend your process to suit the tool | Built around the workflow you already run |
| Time to something useful | Immediate, and permanent | Quick to switch on, slow to make fit | A working slice in weeks, then hardened |
| Who owns the data | You do | The vendor, on the vendor's terms | You do, in your own accounts |
| When it breaks | That person sorts it, if they are in | A support queue and a ticket number | The people who built it |
| What it costs | A salary, every year, forever | Per seat, forever, used or not | Scoped and quoted up front |
Where building companies actually lose hours and dollars.
- Builders run multiple sites at once, and the paperwork behind each one grows faster than anyone has time to manage.
- Variations, supplier orders, and client updates are the admin layer that determines whether a build stays on schedule.
How we build AI knowledge base for building companies.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a building company business, so value lands before the build is finished. Internal AI knowledge base.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing building companies the most hours or the most leads, and deliberately ignore the rest for now.
- Ship a working sliceA narrow version goes into production in weeks, against real work, so the value shows up before the build is finished.
- Prove it, then widenWe measure it against what the work cost before. If it does not pay for itself, we say so rather than scaling it.
- Harden and hand overLogging, fallbacks and a real handover, so it keeps running when we are not in the room.
The outcome for building companies
What changes for building companies after this lands: the work that used to need a person stays done, the work that needs a person gets done with their attention undivided. New staff get to productive 3x faster - less senior-team interruption.
Not your typical AI agency.
Honest about what AI can and cannot do
Ships the one workflow that pays for itself
Hours given back, never the size of the invoice
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
FAQ
What's the realistic timeline for AI knowledge base with a building company?
Most building companies have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational - your team gets to use the thing well before the engagement is "done".
What does AI knowledge base cost for a building company?
Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.
Can you walk us through a comparable build?
Yes - on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. New staff get to productive 3x faster - less senior-team interruption.
What happens if we want to swap a vendor out later?
AI knowledge base is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Claude, Pinecone, Vercel AI SDK, Postgres, MCP are our defaults, but the build is intentionally portable.
Twenty minutes, your call.
You describe what's broken. We'll tell you what we'd actually do about it.
Talk to us about this
Tell us what you're trying to do and we'll reply with how we'd build it - no obligation.