We build financial software for construction: live, project-level financial forecasting for GCs whose projects outgrew their spreadsheets. Built inside your own infrastructure — just like we build for a Top-20 US GC, where our engineers are replacing hand-updated spreadsheet models with real-time forecasting.
Three to four weeks of engineer-led discovery: we map your tools, data, and spend, and return ranked findings, a roadmap, and a working prototype.
Scope your AssessmentYou already run an ERP, a project management platform, and accounting software. Spreadsheets fill the gap between those systems, becoming an integration layer that grew there rather than one anyone built. Here’s what that looks like from the finance seat:
Project financial models live in spreadsheets, updated by hand. Finance spends its week moving numbers between files and reconciling versions instead of analyzing what the numbers say.
Spreadsheet models grow into webs of interdependent formulas where one field is feeding thirty others, those feeding thirty more. Nobody can audit them, and a single broken link skews the forecast behind a multimillion-dollar project decision.
Over- and under-billing positions take days to assemble from job cost data, billing records, and change orders living in separate systems. What should be a live indicator becomes a monthly ritual.
Executive reviews run on snapshots that were accurate last month. What’s our margin on this project, today?’ has no quick answer. There’s no drilling into a project, no adjusting a forecast to see the impact — decisions about your moving projects get made on numbers from three weeks ago.
We build the engineering layer your finance stack is missing, integrated with the systems you already run. Your team keeps the tools they know; what changes is that the numbers underneath are current and consistent.
Per-project burn-down, backlog against pipeline, and key metrics (revenue, cost, forecasted cost at completion) are updating as the data changes. Drill into any job, adjust an assumption, see the impact immediately. Reforecasting stops being a monthly project and becomes a property of the system.
Over/under-billing positions and forecasted cost at completion are calculated continuously from your source systems, reconciled against your GL, and visible per project, per region, and company-wide.
The logic your spreadsheets carry in thousands of interdependent formulas moves into code that calculates, validates, and flags inconsistencies on its own. A broken dependency surfaces as an error, sparing you the hunt for where a wrong number came from.
The point is to connect your existing ERP, project management, and accounting systems, not replace them. If integrations at your company have been “planned for phase three” for years because security won’t sign off, that’s exactly the constraint we build within.
The numbers are not on your side, and none of this is a failure of your finance team. It’s what happens when your project financials live in files that are already stale by the time they’re consolidated.
According to the KPMG Global Construction Survey, one in four projects lands within 10% of its original budget. Forecast quality is a big part of why.
Autodesk and FMI estimate that inaccurate, incomplete, or late data can cost a contractor up to $165M per $1B of annual revenue. That’s roughly 16 cents on every dollar.
The other 75% goes to collecting and reconciling data across spreadsheets — the work a system should be doing.
These workflows are not enterprise exotics. Manual reforecasting, formula webs nobody can audit, WIP assembled by hand: they run in every GC large enough to have five offices and too lean to have an innovation team. Even a Top-20 contractor, with an innovation team and enterprise budgets, chose engineers who live in this domain over building it all in-house.
You get live, project-level forecasting built on patterns that already run in production. They come from over four years of engineering inside one of the 20 largest US general contractors, where we’re replacing hand-updated spreadsheet models with a live forecasting platform.
Deviations become visible while there’s still time to act on them. That’s the working standard, and it’s already paid off: on one of our earlier projects, moving to live forecasts caught a costly design issue that would have cost six figures to fix in the field.
Everything we build runs inside your cloud environment, behind your SSO, and under your access policies. Your IT reviews the access model before the first line of code. It’s the same setup our anchor client’s security team has audited and lived with for years.
Your IT and security teams will ask: who gets access to our financial data, and where does it live?
Our answer: it doesn’t move. We build inside your VPC, authenticate through your SSO, and operate under your access policies. Your security team defines the perimeter; we work inside it. There is no Syndicode-hosted copy of your data, no third-party analytics layer, no data leaving your cloud account.
On our current engagement with a Top-20 US GC, this model has held for four years: no project data has left the client’s cloud environment. Their security team reviewed our access model before the first line of code. Yours can do the same.
If your integrations have been blocked by security for years, this is usually why our conversations with IT go differently: we’re not asking them to trust a vendor’s cloud. We work inside theirs. Nothing new for security to approve but people.
Every engagement starts with a paid, fixed-scope assessment: three to four weeks of engineer-led discovery that maps your tools, your data, and your spend, and returns a specific, prioritized answer.
We inventory the systems your teams actually use: ERP, project management, accounting, and the spreadsheets in between. We trace where financial data originates, where it gets re-entered by hand, and where it goes stale.
We rank findings by financial impact: where manual reforecasting costs the most time, where data disagreements create the most risk, and which fixes return the most for the least engineering.
You get ranked findings, a 90-day and a 12-month roadmap, and a working prototype — a functioning slice of the system, on your data, inside your infrastructure.
The assessment is priced and scoped before we begin. If the honest conclusion is that your current stack is fine, that’s the conclusion you’ll get. The deliverables are yours either way.
One workflow, mapped end to end: project forecasting, WIP, or cost-to-complete. Best when you already know where it hurts and want a costed plan for that specific problem before committing to more. You leave with ranked findings, a roadmap, and a working prototype for that workflow.
Your full finance data landscape: every system where project financials originate, get re-entered, or go stale, and how they connect (or don’t). Best when the problem isn’t one report but the gaps between all of them. You leave with a prioritized picture of the whole finance stack and where the highest-return fixes are.
The finance landscape across regions and entities: where processes diverge office to office, where data can’t be compared, and what a common standard would take. Best for GCs running multiple regions on incompatible spreadsheets and definitions. You leave with a roadmap that accounts for multi-entity structure, not just one office’s workflow.
A three-to-four-week assessment returns ranked findings, a 90-day and 12-month roadmap, and a working prototype — inside your infrastructure, at a fixed price. If the conclusion is that you don’t need us, you’ll have that in writing too.
Scope your AssessmentFour years inside a Top-20 US GC means we’ve built against real WIP schedules, real buyout data, and real month-end pressure.
Average tenure on our team is 4.5 years. No junior hires, by design. The engineers who run your assessment are the engineers who build the system — and, on our track record, still on it years later.
Working inside client infrastructure — VPC, SSO, their policies — is how our largest engagement has run for four years. It’s the default.
No invented ROI figures, no case studies we can’t stand behind. What we claim, we can show. We’re expanding our Data and AI engineering practice the same way we build everything else: proven on real work first.
Sometimes you should. The best construction financial software for your team might already be on the market. If a packaged product matches how your teams actually work and integrates cleanly with your ERP, buying is the faster path, and we’ll tell you so during the assessment.
Custom makes sense when your workflows, data structures, or security requirements don’t fit the packages: when the product would force your teams to change how they work instead of the other way around.
They shouldn’t have to. We build inside your infrastructure: your VPC, your SSO, your access policies. In our current engagement with a Top-20 US GC, no project data has left the client’s cloud environment in four years. Your security team defines the perimeter; we work inside it. They can review our access model before anything starts.
That’s usually what happens when the work starts with a solution instead of an audit. Our assessment starts by mapping the tools your teams actually use, where the data actually lives, and what you actually spend. Then we rank findings by impact.
It’s paid and fixed-scope on purpose: you get a specific, prioritized answer, not an open-ended discovery designed to sell the next phase. If the honest answer is “your current stack is fine,” that’s the answer you get.
Three to four weeks, led by engineers who will later build what they scope. We offer three tiers: Focused, Company-wide, and Enterprise, depending on how much of your tool and data landscape you want covered. Every tier returns the same core deliverables: ranked findings, a 90-day and 12-month roadmap, and a working prototype. Pricing is fixed per tier and confirmed before we start.
Almost nobody’s is, that’s usually finding #1. Assessing the state of your data is part of the assessment, but not a prerequisite for it. You don’t need a data warehouse, a data team, or clean spreadsheets to start. You need to know what you have, and that’s what the first weeks produce.
Not unless you want to. Excel is a fine analysis tool. The problem is when it’s also your database, your integration layer, and your source of truth all updated by hand, by many people, on different schedules. We move the system of record out of spreadsheets; your team keeps using Excel on top of reliable, current data if that’s how they like to work.
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