Outcomes, not slide decks

A VC-backed, cloud-native public safety software company had grown from startup to Next Gen 911 sector leader, but its evolution was incomplete. The lead investor asked the CEO to commission an independent assessment. Techquity found genuine assets — strong team, differentiated product, significant customer base — but the company had drifted into professional services mode: ~60% of engineering was fulfilling contractual obligations rather than advancing the roadmap. A secondary product line had been rushed from prototype to production and was uneconomical to operate despite real market demand. Techquity's recommendations were implemented, a follow-on engagement redesigned the next-generation platform architecture, and within approximately one year the company was acquired by a major public safety technology provider.

A growth equity firm leading a $40M Series C in the dominant digital challenger bank in Latin America engaged Techquity for technical due diligence. Complexity was compounded by a concurrent acquisition of a fintech lending startup — requiring two simultaneous assessments and an integration analysis. Techquity interviewed a dozen engineers and leaders, reviewed codebases, infrastructure, and product processes. The engineering culture was a genuine standout — Amazon-style operating mechanisms rarely seen at this stage. However, Techquity surfaced meaningful concerns: roadmap lacked dependency sequencing; deployment windows caused customer outages; the CPO had recently departed; and the acquisition added integration complexity the roadmap hadn't accounted for. Techquity delivered a nuanced, stage-appropriate picture that gave the investor clear pre-investment conditions to act on.

A national, technology-enabled mental health platform was preparing for a major fundraise when its lead investor grew concerned about tech leadership, team structure, and spend. The investor suggested a Techquity assessment to the CEO, who commissioned it — focused deliberately on leadership, organization, and process, not the technology itself. Techquity found the CTO was not advocating effectively for engineering priorities in senior leadership. The VP of Product operated in a project-based rather than product-led mode. In interviews across the product and engineering organization, multiple people acknowledged having no understanding of the company's strategic direction. The company closed a $130M Series C led by a top-tier venture firm and subsequently replaced both the CTO and CPO.

A US-based startup had built an AI-powered platform enabling creators to monetize content through personalized product recommendations, with revenue models spanning creators, consumers, and brand partners. A growth equity firm engaged Techquity for comprehensive technical and organizational due diligence. A distinctive challenge: the CEO was US-based but the entire engineering team operated out of China — creating language and evaluation barriers most diligence firms could not navigate. Techquity assembled a team that included a dual-national Chinese-American former engineering leader from a major US technology company, enabling genuine access to the engineering organization. The assessment found a modern, AWS-native architecture with a well-isolated AI system — a technically correct design. However, Techquity flagged the absence of mandatory unit tests, over-reliance on manual QA, and a hybrid agile/waterfall process introducing delivery unpredictability. The cross-border team structure, while functional, carried execution risk the investor needed to factor into post-investment planning.

An open-source vector database company had built one of the most technically capable engines for filtered semantic search and retrieval-augmented generation workloads, offering a managed cloud service alongside a hybrid deployment model that allowed enterprises to keep data planes within their own cloud environments. A growth equity firm engaged Techquity to validate the platform's technical leadership and commercial defensibility. The core architecture was exceptional: a Rust-based engine with metadata filtering integrated directly into the vector index — enabling performance on filtered search workloads that competitors struggled to match. The hybrid cloud model was well-positioned for regulated enterprise buyers and supported favorable gross margin potential. Techquity's findings on commercial defensibility were nuanced: the moat was execution quality rather than lock-in, requiring the company to continue outpacing open-source alternatives on capability and reliability. Sales engineering cost per customer was identified as a potential unit-economics constraint for less technically sophisticated buyers.

A startup had built the leading 3D scanning application for mobile devices, enabling users to create high-fidelity 3D models from photos, LiDAR scans, and 360 captures — exportable in over a dozen file formats. A growth equity firm evaluating the company's Series A engaged Techquity for a technical, organizational, and process assessment. The findings were unusually positive for a company at this stage. The architecture earned a 'green' rating: a service-oriented design with strong separation of concerns, a sophisticated cross-platform rendering strategy using a single C++ editor compiled to WebAssembly, and a React Native unification approach giving the team one codebase across web and mobile. Technology choices reflected genuine senior-level depth in a highly specialized domain. The critical risk Techquity identified was economic rather than architectural: the highest-quality model pipeline required heavy GPU compute that would scale linearly in cost with job volume — a material unit-economics constraint at scale. A heavy dependency on a single backend-as-a-service provider was also flagged as platform lock-in risk.

A women's health startup had built a consumer diagnostics platform centered on microbiome testing and personalized care guidance. With a very small engineering team, its growth equity investor — leading the Series A — needed a rapid, focused assessment of whether the technology could scale to support the investment thesis. Techquity found a capable team and a stack appropriate for the current stage, but surfaced a cluster of risks that needed addressing before close: the application ran without a standby database, creating a single point of failure; automated testing was absent, leaving the team dependent on manual QA at every release; and manual handling of third-party API failures would become an operational bottleneck as data volume grew. Rather than a general "needs work" verdict, Techquity delivered a specific, sequenced list of what needed fixing, on what timeline, and in what order — giving the investor a practical post-close roadmap.

A B2B software company had built a vertical SaaS platform for the collision repair industry, addressing the growing complexity of Advanced Driver Assistance Systems calibration — giving shops VIN-specific requirements, OEM documentation, and insurance-ready compliance reports. A growth equity firm engaged Techquity for Series A technical due diligence ahead of close. The stack was appropriate for its stage and load profile, and the team was already aware of its technical debt and actively managing it — a positive signal. Techquity flagged the backend infrastructure as a near-term priority: the company was running on a platform-as-a-service provider that would become operationally burdensome at scale, with a cloud migration already on the roadmap, alongside a gap in data warehousing as the team ran analytics queries directly against production. Techquity delivered a stage-calibrated verdict: a credible team with strong product-market fit and a clear, prioritized set of infrastructure investments for the twelve months post-close.

A UK-based startup had built an API-driven platform aimed at helping rights holders monetize live and on-demand content, positioning itself as API-first infrastructure — a high-ambition framing. A VC evaluating an investment engaged Techquity for a technical and organizational assessment. The findings raised meaningful concerns: for a company positioning itself as API-first, the API design process was ad hoc, without a rigorous versioning strategy or documented contracts; the backend was mid-transition from a legacy stack to Node.js, a signal of frontend-origin engineering with limited deep backend experience; security had gaps in server-to-server authentication; and the roadmap, when pressed, was expressed in aspirational language rather than grounded plans. Techquity gave the investor a frank picture of genuine ambition paired with execution risk concentrated exactly where it mattered most — the reliability and credibility of the API product itself.

An end-to-end AI platform had built computer vision capabilities for supply chain and retail enterprises, helping customers address security, transportation, and operational challenges through vision data. A private equity firm engaged Techquity for technical due diligence ahead of an investment. Techquity assessed the platform's AI and machine learning architecture, computer vision pipeline, enterprise readiness, scalability, and team capabilities — focusing on how much of the platform's AI capability was production-grade versus prototype-stage, how robust the underlying data pipeline was, and whether the company could meet enterprise procurement expectations around security, compliance, and integration. The engagement gave the investor an independent, expert-level read on technical credibility in a specialized domain where few generalist diligence firms have the depth to separate genuine machine learning capability from demo-quality implementations.

An enterprise software company had built a workflow orchestration platform targeting distributed systems teams and, increasingly, AI-native workloads, competing against a well-funded rival. A growth equity firm engaged Techquity to validate the company's enterprise positioning and AI-era differentiation. The architecture proved technically mature: the platform sustained roughly 5,000 API calls per second under load, backed by Redis-based queuing with distributed locks and idempotent worker patterns delivering reliable execution semantics, with each enterprise customer running in fully isolated deployment clusters and disaster recovery holding to a sub-30-minute recovery time objective. The primary risk was roadmap parallelism — multi-region architecture, an agentic platform layer, and a new API gateway all being pursued concurrently, more than engineering bandwidth could likely absorb. Techquity recommended phasing the AI roadmap behind infrastructure stability milestones, validating the investor's thesis while surfacing specific risks for post-close monitoring.

An enterprise software company had built an AIOps platform for network operations teams at large carriers and Fortune-scale enterprises, combining a correlation engine to reduce alert noise, a GenAI layer to narrate incidents in plain English, and per-customer isolation built for enterprise procurement standards. A growth equity firm engaged Techquity for technical due diligence. Per-customer isolation — dedicated databases, storage, and networks per tenant — was a high-signal differentiator for regulated and carrier buyers, and the GenAI narration layer was differentiated but early. Techquity's primary concern was data architecture: four separate database engines created cross-store correlation limitations and governance gaps, with no unified schema registry to enforce controls as the platform grew. Techquity gave the investor a clear map of what was genuinely enterprise-ready versus roadmap ambition, and identified the governance investments needed before the platform could expand its addressable market.

A venture-backed AR and geospatial AI company had built a proprietary 3D mapping platform spanning 170,000+ locations — but had never systematically applied it to enterprise markets. A Techquity partner with deep geospatial roots led the enterprise strategy exploration, then stepped in as acting CPO to run the product function and navigate a major structural split between the games business and the geospatial platform. Techquity also supported the search for a permanent CPO. The strategic clarity produced — combined with rapid AI advances — crystallized a consequential decision: separate the two businesses entirely.

A large, globally operating engineering and construction firm had reached an inflection point. Its CEO had set a clear mandate: become a digital innovator. The existing IT organization — technically capable but built entirely around maintaining systems rather than building products — was not positioned to deliver. Techquity's assessment found that ~81% of IT personnel were external contractors, 97% of the IT budget was consumed by keeping existing systems running, and projects labeled "digital innovation" were largely legacy replacements. Techquity's role grew substantially: redesigning and hiring a new Chief Digital and Technology Officer (a hybrid CIO/CTO reporting to the CEO), serving in an interim co-pilot capacity during the transition, leading the CISO search and providing interim CISO coverage, conducting a deep-dive spend and vendor management analysis, and designing a world-class Digital Product Operating Model. The relationship has continued for more than two years and remains active.

A scaled accessibility technology company providing communication services for a large underserved community had undergone a PE ownership change and faced a critical strategic moment: its future depended on successfully building an AI-based automation capability that would fundamentally change how its services were delivered. Techquity's assessment found a culture of distrust developed across multiple leadership changes, engineering leaders tolerating underperformance without accountability, and a pattern of learned helplessness across senior leaders who understood the problems but had stopped believing they could fix them. The AI initiative — the company's most important technical bet — was structurally misorganized, without the focused single-threaded ownership it required. Techquity's engagement expanded into multiple workstreams: a deep dive into the AI initiative's science, technology, and organizational design; a CEO copilot role; recruiting engagements to place a dedicated AI leader and a product leader; and a vendor and spending analysis.

A multi-brand consumer company operated a collection of content, commerce, and manufacturing assets centered on home goods and culinary culture. Commerce drove more than 95% of revenue, and leadership wanted to reposition toward a content and advertising-led model built around social commerce rather than owned website traffic. Techquity was engaged to assess the current technology state and build the strategy and roadmap to support the transformation. The current-state assessment was candid: no data team, no single source of truth, no meaningful analytics infrastructure, and critical workflows still running on manual data transfers and spreadsheets, with e-commerce, ERP, manufacturing, and warehouse systems operating in fragmented silos. After interviewing more than 50 staff across the business, Techquity built a phased roadmap: consolidating the commerce stack, building data infrastructure from scratch, enabling social commerce across major platforms, and shifting the revenue mix toward roughly 60% commerce, 30% advertising, and 10% wholesale by 2029 — while refocusing in-house manufacturing on limited-run signature designs rather than production scale.

A B2B vertical SaaS platform serving the collision repair and ADAS calibration industry became one of Techquity's clearest examples of a compounding advisory relationship. Techquity conducted the Series A technical due diligence for the lead investor ahead of close, identifying a credible team with strong product-market fit and a clear set of infrastructure investments needed post-close. Following the investment, a Techquity partner maintained an ongoing advisory relationship with the CEO and CTO — supporting architectural decisions, team building, and product strategy as the company scaled. Two years later, Techquity was retained by the company itself to conduct a pre-transaction technical assessment. This gave the company an investor-grade view of its own readiness across architecture, data, security, operations, and team execution before an investor or acquirer's diligence team would see it. Techquity identified gaps, prioritized remediation, and strengthened the documentation supporting a credible technical narrative.

A college sports recruiting platform was poised to acquire a profitable athlete showcase marketplace with over 1.2 million athlete profiles and relationships spanning more than 3,000 college programs. A private equity firm was leading a $40M investment in the combined entity. Techquity conducted the pre-investment technical due diligence and was subsequently retained for a structured twelve-month post-close monitoring engagement, tracking execution against the issues identified during diligence. The diligence found strong engineering culture built primarily around a single sport, with multi-sport readiness incomplete and the data model requiring meaningful refactoring to support other sports cleanly. The acquisition added athlete identity resolution complexity — merging user bases without a clean identity layer risked integrations that looked functional but would fail at scale — and monetization infrastructure needed deliberate sequencing ahead of the combined subscription model. The quarterly monitoring engagement gave the investor structured, ongoing visibility into execution, flagging sequencing discipline ahead of peak seasons — pairing pre-investment diligence with post-close accountability in a distinctive model for investor oversight.