# ProjectData - full site summary Source: https://projectdata.dev Language: English Scope: factual description of ProjectData's positioning, services, experience and site structure. Nothing in this file is inferred or estimated. --- ## 1. What ProjectData is ProjectData is a technology company that builds custom data, AI, automation and software systems for companies with complex business and operational problems. Central positioning: We build the data, AI and software systems behind better business decisions. Supporting statements: - We turn fragmented data, manual processes and complex technology problems into systems that actually work. - We build the technology layer your business is missing. ProjectData is not a generic software outsourcing agency, a body-leasing company, a staffing provider or a generic digital-transformation consultancy. It does not sell developer headcount. It sells the ability to solve a difficult business problem: understanding it, designing the architecture, and building and integrating the solution. ## 2. Who ProjectData works with Typical decision-makers and audiences: - CEO, COO, CIO, CTO, Chief Data Officer - Head of Digital, Head of Operations, Commercial Director - Supply chain leadership and senior technology leadership - Investment professionals evaluating technology-intensive businesses Industries where ProjectData is most relevant: - Retail and FMCG - Manufacturing - Distribution and logistics - Financial services - Travel - Other data-intensive or operationally complex businesses A typical strong-fit customer is a mid-market or enterprise company. ProjectData works with companies in Romania, across Central and Eastern Europe, and in the wider European market. Engagements run remotely with on-site presence where the problem requires it. ## 3. When a company should consider ProjectData - Business data is fragmented across multiple systems. - Management lacks timely visibility into the business. - Important processes still involve spreadsheets and repetitive manual work. - ERP, CRM, WMS, POS, e-commerce, marketing, finance and internal systems do not communicate properly. - Existing SaaS solves only part of an important workflow. - The company wants practical AI applications rather than demos. - An important technology initiative lacks internal capacity or specialised expertise. - The problem requires understanding both business workflow and technology. ## 4. Services ### 4.1 Data & Intelligence Headline: turn fragmented data into decisions. ProjectData connects information across existing systems and builds the data infrastructure and applications required to make it useful. The stated position is: we build the intelligent layer on top of the systems you already have. ProjectData does not normally replace ERP, CRM or other core systems. Capabilities: data architecture; data platforms; data pipelines; ERP, CRM, WMS and POS integrations; API integrations; real-time and near-real-time data; management dashboards; business intelligence; sales intelligence; operational analytics; forecasting; marketing-to-sales attribution; decision-support systems; custom data applications. Featured use case - marketing spend to actual revenue. The flow is: marketing spend → leads and customers → CRM → sales → ERP/POS → revenue → margin → better decisions. Many businesses can measure campaigns, clicks and leads but cannot connect that information to sales, revenue, margin, inventory or customer outcomes held elsewhere. ProjectData builds the integrations, identity model, attribution approach and pipelines that join those worlds, enabling reporting on revenue and margin by channel and campaign, reduced reporting delay and near-real-time visibility where it is justified. Other Data & Intelligence use cases: operational visibility; sales intelligence; inventory and supply analysis; forecasting; management reporting reconciled against finance; and custom data applications such as pricing, allocation and planning tools. Approach to data projects: start with one decision the business currently makes blind; map which system genuinely owns each piece of information; build a thin slice in production rather than a prototype on an extract; reconcile output against a source the business already trusts before building on it; then extend. ### 4.2 AI & Automation Headline: AI that works inside the business. Principle: start with the business problem, not the model. Design order: business problem → workflow → data → AI → operational outcome. Capabilities: AI applications; AI agents; internal copilots; intelligent workflows; document processing; knowledge systems; sales automation; customer-operations automation; decision support; process automation; AI-enabled internal tools; AI evaluation systems. AI becomes useful when it is integrated with real company data, workflows and systems. Moving from proof of concept to production requires: integration with systems of record rather than extracts; explicit handling of data quality; respecting the organisation's existing permission model at query time; a repeatable evaluation set with separate quality dimensions; defined behaviour when a model is slow, unavailable or returns unusable output; designed human oversight where the cost of error is real; and monitoring of quality, latency, cost per outcome and usage. ProjectData states plainly when AI is the wrong answer: when the process requires deterministic certainty, when the underlying data is not ready, or when volume does not justify the cost of building and maintaining the system. ### 4.3 Custom Enterprise Software Headline: software for problems that don't fit inside a standard product. Capabilities: internal business applications; mission-critical platforms; custom operational software; workflow systems; enterprise integrations; cloud applications; legacy modernisation; complex platform engineering; bespoke systems connecting departments and platforms. Custom software is justified when the workflow is genuinely specific and commercially meaningful; when the integration between systems is itself the requirement; when generic software is creating manual work; when the system is a strategic internal platform; when a legacy system constrains an important process; or when off-the-shelf software covers most of a process but not the part that matters. Custom does not mean rebuilding everything. In most engagements the ERP, CRM and other systems of record remain in place and what gets built is a focused layer that connects them and holds the missing workflow. Principles: the client owns the code and infrastructure; the smallest system that solves the problem properly; working software in front of real users early; documentation and handover treated as part of the build; conservative technology choices by default. Delivery time depends on scope, the number of integrations and workflow complexity. ProjectData does not publish standard project timelines. ### 4.4 Technology Advisory (secondary offering) Framing: independent technical judgment for important business decisions. Services: technical due diligence; architecture reviews; technology strategy; AI and data strategy; technology investment reviews; fractional CTO / technology advisor. Audience: investors, private equity, founders, CEOs, boards and portfolio companies. Technical due diligence review areas: architecture; scalability; technical debt; cloud infrastructure; engineering organisation; delivery process; data architecture; high-level security posture; roadmap realism; technology investment requirements. Explicit limitations: ProjectData does not perform formal security audits, penetration testing, regulatory or compliance certification, and does not claim any security or cloud certifications. ## 5. How ProjectData works Philosophy: start with the problem, build only what matters. Process: Understand → Map → Design → Build → Integrate → Improve. 1. Understand - the business problem and its economic or operational impact. 2. Map - the data, systems, people and workflows involved. 3. Design - the smallest architecture capable of solving it properly. 4. Build - software, integrations and data infrastructure, in working increments. 5. Integrate - connect the solution into the real business environment. 6. Improve - measure the effect and extend where additional value exists. Engagements are staffed with senior people who do the work themselves. Clients work directly with the people designing and building the solution. ProjectData does not make published claims about current team size. ## 6. Experience The following are factual, high-level descriptions. Implementation details, metrics and outcomes are not published, and several clients are confidential. - **Financial services - anti-money laundering system.** Experience designing and building an anti-money laundering system for a top bank. Enterprise banking environment, mission-critical workflows, complex software, integrations and operational requirements. No implementation details, compliance claims or return-on-investment figures are published. - **Enterprise data - monitoring and tracking platform.** Built a platform used for monitoring and tracking large enterprise customers across multiple countries. The client is not identified. - **Mobility - car rental platform.** Designed and built a custom platform supporting car-rental business workflows. - **Cloud infrastructure - cloud platform engineering.** Experience leading the engineering effort to build a cloud platform from the ground up. ProjectData does not own or operate a public cloud provider. - **AI - AI product and evaluation systems.** Experience building systems and workflows around AI chatbot testing and evaluation. ## 7. Frequently asked questions **Does ProjectData replace an existing ERP or CRM?** Normally no. It builds the intelligent layer on top of systems already in place. Replacing a core system is treated as a separate, deliberate decision. **Can ProjectData connect ERP, CRM, WMS and marketing data?** Yes. Integration across systems that were not designed to cooperate is the most common work it does, through APIs, databases, message queues, files or middleware. **Can business data be available in near real time?** Often, but it is treated as a design decision sized to the decision being made and to what source systems can sustain, rather than as a default. **What if data quality is poor?** That is the usual starting position. Validation, duplicate and gap handling, and reconciliation against a source the business already trusts are treated as part of the build. **Where should a company start with AI?** With a process that is expensive, repetitive and measurable, narrow enough to be in real use within weeks. **Does an AI application need access to company data?** To be useful inside a business, almost always - scoped deliberately and honouring the existing permission model. **When should a company build custom software instead of buying SaaS?** Buy when the process is standard, the product mature and the process not a differentiator. Build when the workflow is specific and commercially meaningful, when the integration is the requirement, or when the gap in a generic product is creating significant manual work. **How long does a custom software project take?** Scope, integrations and complexity determine delivery time. No standard timeline is published. **Who owns the code?** The client. Source code, infrastructure definitions and documentation belong to the client. ## 8. Contact Email: office@projectdata.dev Contact form: https://projectdata.dev/contact/ Preferred framing for contact is a conversation about a business problem rather than a quote request or product demo. ## 9. Site map - https://projectdata.dev/ - homepage - https://projectdata.dev/solutions/ - solutions overview - https://projectdata.dev/solutions/data-intelligence/ - https://projectdata.dev/solutions/ai-automation/ - https://projectdata.dev/solutions/custom-software/ - https://projectdata.dev/technology-advisory/ - https://projectdata.dev/work/ - selected work index - https://projectdata.dev/work/anti-money-laundering-system/ - https://projectdata.dev/work/enterprise-monitoring-platform/ - https://projectdata.dev/work/cloud-platform-engineering/ - https://projectdata.dev/work/car-rental-platform/ - https://projectdata.dev/work/ai-evaluation-systems/ - https://projectdata.dev/how-we-work/ - https://projectdata.dev/about/ - https://projectdata.dev/insights/ - https://projectdata.dev/insights/connecting-marketing-data-to-actual-sales/ - https://projectdata.dev/insights/custom-software-or-saas/ - https://projectdata.dev/insights/ai-proof-of-concept-to-production/ - https://projectdata.dev/contact/ - https://projectdata.dev/privacy/ - https://projectdata.dev/cookies/