Our Work
Selected case studies and industries from real collaborations.
Problem
In a partner organization, generic AI training wasn't changing how a team actually worked, and manual coordination across email, text, and CRM systems eats the time AI was supposed to free up. Adoption stalled further without a governance framework from leadership on how the tools were deployed and used at scale.
Solution
We trained employees role-specifically on AI tools inside their own job, then built AI directly into how work moves through the company, removing repetitive, low-judgment tasks so people spend time on decisions that require a person. We deployed a custom, company-branded AI harness that receives incoming communication across email, text, and CRM notifications and updates the right systems automatically, replacing manual work across four tools. Governance was built in from the start, with role-gated access, given choices of model provider up to self-hosted open-source, and an AI and data use policy the client defined rather than inherited from the modeel provider. The rollout ran phased, with Trailrock team members embedded in day-to-day operations throughout.
Professional ServicesPrivate Equity
Description
Our team built integration points around entrenched platform vendors rather than replacing them, connecting property, lease, tenant, deal, and financial data into a single source of truth. This carried deal-sourcing and pricing models and geospatial analysis down to individual properties for investment-side clients. The result was the company transacting more, closing faster, and seeing across the portfolio without reconciling five systems by hand.
Quantitative InvestmentGeospatial AnalysisMaster Data Management
Problem
A 3rd party consultancy's systems inventory can tell a deal team what a target owns, but not what breaks when two companies' data and processes actually connect, potentially missing the undocumented workarounds that quietly keep a target running are the usual cause of post-close breakage and the attrition that follows it.
Solution
We deliver assessments built for the CEO, the deal team, and the PE sponsor: a dollar figure for technical integration, a realistic timeline, and a risk-tiered list of systems, covering how each company's data and business model actually connect and surfacing the undocumented workarounds a target's systems quietly support. Engagements run before close, where findings inform valuation and deal structure, and during or after close on integrations that had already gone sideways, with assessments feeding directly into a roadmap and Trailrock staying on through implementation.
Description
We designed systems that track subcontractor and supplier terms well enough that contracts execute the way they were written.This system reconciles multi-jurisdiction compliance against actual project data and pulled fragmented site and plant data into one operating picture. In other companies, we designed and deployed custom computer vision models that assess incoming mixed-load composition in the field, replacing a scarce specialist's judgment call with a repeatable process used for pricing and vendor negotiation.
Geospatial AnalysisIoT Data StrategyMachine Learning / AI Modeling
Problem
For a commercial real estate company, off-market opportunity represented the highest margin transactions. These opportunities are hidden in various ways including layered legal entities built to obscure who owns what, and traditional underwriting can't source or price it faster than the market moves. This made sourcing these deals difficult, and largely dependent on random circumstance.
Solution
We built data pipelines pulling public and proprietary sources, public records, permits, imagery, ownership and debt records, demographics, traffic and transit data, into custom machine learning models for deal sourcing and pricing, with the deepest track record in commercial real estate, particularly industrial assets. Entity-resolution algorithms grouped ownership across the layered legal entities that obscure who owns what: one deal that looked like $15 million on the surface turned out to be an $80 million opportunity, one other firms never found because it wasn't listed as one. Models, pipelines, and synthesized data belong to the client, hosted on the client's infrastructure or Trailrock's, and keep running after the collaboration ends.
Commercial Real EstatePrivate EquityFinance
Description
Trailrock orchestrates diligence so triggers, handoffs, and approvals stay visible across the deal team and outside advisors. We run technical due diligence that prices integration cost and risk before close and carry the data model built during diligence into the portfolio company so it generates value from day one post-close. This carries to the same platform engineering and workforce work inside portfolio companies to execute the investment thesis directly.
M&A Technical Due DiligenceQuantitative InvestmentAI Implementation
Problem
A commercial real estate client had a dozen disconnected logins and vendor tools which forced people to work around the software instead of through it, with no single system reflecting how the business actually operates. Information became fragmented and the company lost trust in reporting due to inconsistent and inaccurate metrics.
Solution
We built a Custom Enterprise OS around the client's actual processes and Liquid Ontology, replacing those disconnected logins with one company-branded interface. For a financial-sector client, it drove an 8x increase in measurable output with the same overhead, sustained over a year. The software, data, and systems belong to the company and keep evolving on its own terms after the collaboration ends.
Description
Our team builds a system tying clients, matters, documents, and relationships together, replacing the folder structure a partner had to remember. This added visibility into where a matter stands so a dropped handoff surfaces before the client calls to ask. For trust-sensitive firms, this delivers governance fit to the firm's risk profile, including on-premises model deployment and role-gated access. The result is more clients and matters absorbed without hiring around a broken process.
Legacy ModernizationCustom Enterprise OSAI Implementation
Problem
A customer had legacy systems deeply integrated into their business which had failed to keep up with the operational model. The system's business logic was outdated to the company's actual everyday operations, whcih resulted in employees creating shadow processes to make up for the limitation of the system. This resulted in operational blindspots and friction whenever the company tried to execute new process, onboard new employees, or adapt to market changes.
Solution
We reverse-engineered the undocumented data formats, built connectors for systems with no real API, and extracted the business logic that existed only in old code. Interviews and Talent Profiling captured the knowledge of the people who ran the old system before it was lost, and their judgment shaped the migration plan. Migrations ran phased, not as a single high-risk cutover, cleaning, re-modeling, or carrying forward data case by case with the client. One migration delivered independently verified savings of $600,000 in the first year for a team of 80; another was called instrumental to the company's next ten years of growth by the client's CEO.
Description
Trailrock builds interconnection points around legacy cores, rather than replacing them, abstracting data into a single source of truth that merges and resolves it across every system that touches it. We deploy AI inside the firm's own protected environment, on-premises where warranted, with role-gated access, for institutions whose data-handling requirements rule out off-the-shelf tools. This helps the institution write their own AI and data use policy rather than inherit one from a vendor. The result is consistent operation at scale and a servicing book that holds together through transfers.
Custom Enterprise OSMaster Data ManagementM&A Technical Due Diligence
Problem
The client had a unique investment thesis that required information from a variety of silo'd sources. Physical-location data, satellite imagery, permits, traffic, ownership records, sat scattered across systems that were never built to talk to each other, leaving desirability and accessibility judgments to instinct rather than evidence.
Solution
We built the full geospatial pipeline, collecting and processing satellite imagery, parcel, permit, traffic, and route data into working machine learning and quantitative models, including multi-modal connectivity algorithms that score physical accessibility across freight, air, and road networks in a single index, and image segmentation models that count shipping containers and trucks in satellite imagery as a leading indicator of supply chain shifts. For a commercial real estate client, this became an interactive desirability heat map at property-level granularity across 10,000+ geographic points, wired directly into the client's own operations platform. The same capability applies wherever physical location drives the business: logistics, land use, natural resources, energy.
Commercial Real EstateInfrastructure & Civil Engineering
Description
We connected estimating, scheduling, procurement, and accounting into a single source of truth across the project lifecycle.This tied operational timeline data to job costing for real-time margin visibility by project. The result was unified historical job-cost data with current material and labor costs to turn bidding into a data-backed process, with the estimator's judgment still in the loop.
Master Data ManagementMachine Learning / AI ModelingIoT Data Strategy
Problem
A client had a building-systems product which generated high-volume, geographically diverse data across jobsites that needed to be coordinated with a centralized cloud system. This results in more readings than a cellular uplink or a vendor dashboard can sensibly carry, and this threatened the products cloud-connected viability and the maintenance systems it should inform.
Solution
We engineered for the sensor-data constraints directly, specifically custom data serialization for small payloads, device-level aggregation, and mesh designs where devices coordinate before touching a cellular uplink. For the building-systems client, this took shape as an architecture where sensors submit readings to elected host nodes using a Kademlia-based self-discovery mechanism. IoT was treated as a data problem first, with sensor data designed to feed the client's broader system of record alongside financial, operational, and maintenance data, not a vendor's dashboard silo.
Infrastructure & Civil EngineeringConstruction
Problem
Within a construction company, the same vendor or customer existed under different IDs across systems from different operational divisions. The financial data doesn't tie out between platforms, and true margin and customer lifetime value become near impossible to determine.
Solution
We resolved the same vendor or customer existing under different IDs across systems, reconciled financial data that didn't tie out between platforms, and built the pipelines and central source of truth connecting ERP, accounting, and operational systems. Change management stayed central to the work, because a clean data model only stays clean if the people entering data have a reason to keep it that way. For the construction client, that meant tying operational timeline data to job costing for real-time margin visibility by project, replacing a retrospective quarterly view.
ConstructionFinanceCommercial Real Estate
Problem
A material reclamation company relied on subjective human inspection which opened the door to a different standard every time. Off-the-shelf models trained on public data don't reflect a client's own product, process, or materials, and couldn't achieve the level of accuracy and consistency needed for the industrial application.
Solution
We built custom predictive and generative models trained on the client's own data, not public data, with thesis, testing protocols, and target metrics agreed before work starts. For the materials application, that meant custom neural networks for image classification and regression, adapting a pretrained model through transfer learning and extending a limited dataset with synthetic training data from video-derived point clouds. The resulting model applies the same standard every time, holding up in quality disputes and audits, and ships wired into the client's platform, not as a siloed data science project.