Construction · Development · Trades
Win more of the right bids, underwrite faster, and own the data that raises your multiple.
On-premise AI agents that pre-qualify RFQs, draft proposals from your own past work, underwrite deals from your own cost history, and turn every drawing, RFI, change order and daily log into a second brain — running on hardware you own, so the knowledge that makes you valuable stays yours.
The problem
Your best estimator’s head is the company’s most valuable asset — and its biggest risk.
A contractor’s edge is in what it knows: which bids it wins and why, what a scope really costs in this market, which subs show up, where the margin hides in a change order. Most of that lives in a few people and a shared drive of PDFs. Every RFQ package is read by hand, every proposal is rebuilt from the last one, and every underwriting memo starts from a blank page.
The tempting fix is a construction AI subscription. But the moment your bid history, unit costs and subcontractor scorecards flow through a vendor’s API, they stop being yours alone — they become the vendor’s training data and, eventually, your competitors’ benchmark. The data that would make AI most valuable is exactly the data you cannot afford to give away.
And it compounds. Every retirement takes a slice of the archive’s meaning with it. Every owner who wants to sell discovers that a company whose knowledge walks out at five o’clock is priced like one.
The architecture
A second brain, a team of agents, and an agent that runs them.
The source of truth. We load your project records — drawings, specs, RFIs, submittals, change orders, daily logs, estimates, bids and outcomes, subcontractor performance, closeout packages — into a governed knowledge base with retrieval over text and documents alike. GLM-5.3-Flash reads drawings and scanned forms natively, so the paper archive joins the searchable one.
Specialist agents as written skills. Each workflow is a skill with a procedure: the pre-qualification checker scores an incoming RFQ against your win history, bonding capacity, geography and crew availability and returns a go/no-go with reasons; the proposal drafter assembles qualification statements, safety records, insurance responses and narrative from your past submissions; the underwriter builds a deal memo from comps, cost history, schedule risk and sub performance; the RFI and change order assistant drafts responses with the contract clauses attached. Skills are yours to read, edit and extend.
An agent that runs the agents. A supervising agent owns outcomes and schedules: it watches the bid inbox, dispatches the specialists, checks their work against the source of truth, runs the nightly crons (new RFQs scored by 7 a.m., weekly sub-availability calls, monthly cost-code drift report), and escalates to a person only where judgment is needed. Every action is logged; every draft carries its sources.
Fine-tunes and LoRAs, inside the building. After the first quarter of use, adapters trained on your proposals, estimates and correspondence teach the model your voice, cost codes and standards. Training runs on your hardware; the adapters are yours, and no one else’s model gets smarter from your work.
Sovereign by construction. Open-weight models on a single node in your office or a domestic colocation facility. No data egress, SSO and role-based access by project and by role, full audit logging, and air-gapped operation available for public-sector and defense work.
What it is worth
From a contractor to a software-enabled company.
Buyers and lenders price construction businesses on repeatability and transferability. A firm whose bidding, estimating, underwriting and project knowledge run on documented workflows, proprietary data and software it owns is a different asset from one whose knowledge lives in three people. The workflows transfer with the sale; the data moat — years of priced bids and outcomes, sub performance, unit costs by market — cannot be bought elsewhere.
That is the argument for owning the stack rather than renting it: the same agents that win the next bid are the evidence, at exit or refinancing, that the company runs on systems rather than heroics.
Weeks 1–2
Assessment
Data classes, bid pipeline, systems inventory, obligations. Written architecture and cost model.
Weeks 3–8
Second brain + first agents
Archive loaded, retrieval live, pre-qualification and proposal skills running against real RFQs.
Quarter 2
Underwriting, crons, LoRAs
Deal memos, scheduled runs, adapters trained on your voice. Managed operations from there.
Questions we get
Frequently asked questions
What does AI actually do for a general contractor or developer?
Four things, in order of payback. It reads every RFQ, RFP and bid package the day it lands and scores it against your pre-qualification history, so estimators spend their week on the bids you can win. It drafts proposals, qualification statements and safety and insurance responses from your own past submissions, so nothing is written twice. It underwrites deals — pulling comps, cost histories, schedule risk and subcontractor performance from your records into one memo. And it turns the archive of drawings, RFIs, change orders, daily logs and closeout packages into a second brain the whole company can ask questions of.
Why on-premise rather than a construction AI SaaS?
Because the value is in your data: unit costs, margins, subcontractor performance, what you bid and what you won. Pushed through a vendor’s API it becomes their training data and their pricing benchmark. Kept on servers you own, it stays a proprietary asset — one that buyers and lenders price into your business. Open-weight models like GLM-5.3-Flash run on a single node in your office or a domestic colocation facility, with no vendor in the loop.
What is an “agent that runs agents” in this context?
A supervising agent that owns an outcome — “get this RFQ to a go/no-go recommendation by Thursday” — and dispatches specialist agents (the pre-qualification checker, the takeoff reader, the sub-availability caller, the proposal drafter) on a schedule, checks their work against your source of truth, and escalates to a person only where judgment is needed. Each specialist is a skill with a written procedure; the supervisor is what makes them a team. It runs on the same self-hosted frameworks we deploy for other clients.
How does this raise a valuation multiple?
Contractors trade on thin multiples because revenue is project-by-project and knowledge lives in people. A company whose bidding, estimating and underwriting run on documented workflows, proprietary data and software it owns looks to an acquirer or lender like a software-enabled business: repeatable, transferable, less key-person risk. Buyers pay for that, and the data moat — years of priced bids and outcomes no competitor has — is what they cannot replicate.
What about fine-tuning and LoRAs — do we need them?
Usually after the first quarter. Retrieval over your archive gets you 80% of the way on day one. Fine-tuned adapters (LoRAs) trained on your proposals, estimates and correspondence teach the model your voice, your cost codes and your standards, so drafts land closer to final and estimators trust them. Training happens inside your environment on your own data; the adapters are yours.
Go deeper
Fine-tuning & customization
LoRA adapters trained on your proposals and estimates, inside your environment.
OpenClaw 2.0 vs Hermes Agent
The self-hosted agent frameworks behind the agent-of-agents pattern.
Deploy GLM-5.3-Flash on-premise
The single-node, MIT-licensed model that reads drawings and drafts proposals.
What on-premise LLM deployment costs
Hardware tiers and the break-even against metered APIs.
Engineering & manufacturing
The neighbouring pattern: the process archive as an internal expert.
Hosting & maintenance
Managed operations, evaluations and model upgrades once the agents are live.
Turn the bid pipeline into a system you own.
The two-week sovereignty assessment maps your bid data, project records and systems, and hands you a written architecture with a real cost model — and a view of what the company is worth with it.
Book a sovereignty assessment