Data Preparation for AI
AI fails without the right data. Transform raw, scattered information into a foundation that keeps your initiatives out of pilot purgatory.
From Raw Data to AI-Ready: The Most Important Questions Answered
We have massive amounts of data and access to top-tier models like GPT-4. Why can't we just point the AI at our data right now?
Because raw enterprise data is “noisy.” AI models act like new employees: if you hand them a library of unorganized, conflicting documents, they will fail. Without a structured “semantic layer” (data preparation), the AI cannot distinguish between an outdated invoice from 2018 and a current contract.
Skipping this step leads to “hallucinations” (AI making up facts), and 85% of AI projects fail to deliver value because they skip this foundational engineering.
How does data preparation directly impact my AI ROI and time-to-value?
Data preparation is the foundation of AI velocity and business impact. While it typically consumes the majority of initial AI project effort, with a proper data foundation, this drops dramatically. Your teams can launch new AI use cases significantly faster because they’re reusing structured, governed data assets instead of starting data wrangling from scratch for every initiative.
Organizations with prepared data infrastructure reach market 37% faster than those attempting to “retrofit” data later. “AI High Performers” attribute 41% average ROI to their initiatives specifically because they invested in data readiness first. Additionally, poor data quality costs organizations an average of $12.9M annually in wasted resources and failed pilots.
Why do our successful pilots fail to scale? Why is data the bottleneck?
This challenge – called “PoC Hell” – occurs when successful pilots fail to scale because data infrastructure isn’t designed for reuse. Each PoC typically builds its own custom data pipelines, governance, and integrations, making it extremely expensive and risky to move beyond the lab.
Breaking free from PoC Hell requires reusable data infrastructure built from the start. We map your data landscape to identify critical sources for your highest-impact AI use cases, then create standardized pipelines, quality controls, and access permissions that serve multiple initiatives simultaneously. We also establish clear data ownership and stewardship so quality is maintained continuously, transforming data from a project cost into a strategic asset that accelerates your entire AI roadmap.
What does 'data readiness for AI' actually mean, and how do you assess it?
Data readiness for AI means your data can be reliably retrieved and acted upon by your AI systems – whether LLMs, predictive models, or agentic workflows – in a secure and governed manner. AI-ready data is structured, contextualized, traceable, and accessible on demand.
We assess across six key dimensions:
- Accessibility (Can your AI platforms reach the data they need?)
- Quality (Is data complete, consistent, and free of bias?)
- Governance (Do you have clear ownership, access controls, and lineage for audit?)
- Compliance (Are privacy and regulatory requirements embedded?)
- Context (Is data enriched with business metadata?)
- Scalability (Can your infrastructure handle growing AI demands?)
Our discovery workshops and readiness assessments map your current state, identify quick wins, and create a prioritized roadmap while benchmarking your maturity against industry peers.
How does data preparation specifically prevent 'hallucinations' and improve AI accuracy?
Through a technique called RAG (Retrieval-Augmented Generation).
We don’t just “train” the model; we give it a reference library. Data preparation involves chunking documents into small, searchable pieces (vectors). When a user asks a question, the system first retrieves the exact correct company policy and forces the AI to answer only using that source.
This reduces error rates from approximately 20% (public models) to less than 2% (grounded enterprise models). Clean, traceable data significantly improves model accuracy and reduces the risk of AI failures that could damage your brand or customer trust.
How do you ensure our AI systems are compliant (GDPR, EU AI Act, industry regulations) and trustworthy?
Compliance and trust are built into the data foundation, not added as an afterthought.
We implement three core practices: Data Lineage & Auditability (every transformation is tracked so you can trace back exactly which data drove any AI decision – essential for GDPR right-to-explanation and EU AI Act requirements), Governance & Access Control (role-based and attribute-based access controls ensure sensitive data is only accessible to authorized systems), and Privacy by Design (data masking, anonymization, and differential privacy are embedded where required, with compliance artifacts like consent logs and impact assessments maintained).
Modern data pipelines include PII redaction before data ever touches an AI model, and enterprise agreements ensure your data is never used to train public models. The result is AI that scales confidently with clear evidence that data handling meets regulatory standards.
Clients that trusted us
What our clients say
The communication process was smooth, which assured us that Addepto was devoted to preparing a high-quality product.
Director of Machine Learning – Wealth Management Company – Wealth Management Company
The collaborative culture during the project was unique and significantly facilitated seamless implementation. Addepto has completely satisfied our requirements.
Dawid Majcherek – Nexteer Advanced Technology Engineer – Nexteer Automotive
Addepto’s flexible team reacts to tasks rapidly.
Head of IT – SMEO S.A.
Data Preparation Process Step-by-Step
A Structured Approach To Data Preperation
Data Quality & Accuracy
This foundation ensures that data is clean, consistent, complete, and free from bias through processes like validation, deduplication, error detection, and quality scoring. High-quality data prevents compounding errors in AI models and strengthens confidence in automated decisions.
Data Accessibility & Integration
This stage unifies data scattered across legacy systems, SaaS tools, APIs, and cloud platforms into a single, standardized environment. By removing silos and streamlining ingestion, AI systems gain complete visibility rather than relying on fragmented or incomplete information.
Data Context & Enrichment
Raw data is enhanced with business meaning through metadata, taxonomies, labels, and domain-specific transformations. Contextualization ensures that AI not only processes information but interprets it correctly within the realities of the business.
Data Governance & Compliance
Governance frameworks ensure safe, compliant, and explainable AI through lineage tracking, access controls, privacy mechanisms, and adherence to regulatory standards such as GDPR and the EU AI Act. This foundation reduces risk while enabling transparent, audit-ready decisioning.
Data Infrastructure & Automation
Scalable, automated data pipelines and DataOps practices keep data fresh, monitored, and production-ready without manual maintenance. Automation turns data preparation into a durable capability that supports repeated AI use cases efficiently.
Why work with us
AI and Data Experts on board
Databricks certified Experts
We are part of a group of over 200 digital experts
Different industries we work with
Recognitions & awards
Data Use Cases Across High-Impact Industries
Aviation & MRO (Maintenance, Repair, Operations)
Intelligent MRO Manuals – Processing complex technical documentation to extract tables and diagrams into a structured format preserves context, ensuring mechanics receive precise, citation-backed answers that maintain 100% regulatory compliance.
Predictive Fleet Health – Normalizing decades of historical flight logs and pilot notes enables AI models to identify non-obvious safety patterns across the fleet, allowing for preemptive maintenance that prevents expensive Aircraft on Ground (AOG) events.
Automotive R&D & Supply Chain
Telemetry-to-Text Contextualization – Aligning massive streams of vehicle sensor data (CAN bus) with engineering bug reports creates a unified search layer, enabling engineers to query complex test scenarios using natural language and significantly accelerating vehicle validation cycles.
Supply Chain Intelligence – Restructuring messy, non-standardized supplier contracts and invoices into a unified Knowledge Graph empowers AI Agents to autonomously flag risks and price discrepancies across thousands of global vendors.
Advanced Manufacturing
Operator Copilots – Ingesting and structuring thousands of pages of technical manuals and shift logs into a vector-ready knowledge base enables AI assistants to instantly guide operators through complex repairs, reducing diagnostic time by up to 40%.
Visual Quality AI – Building pipelines to label and standardize raw video feeds from assembly lines creates high-fidelity training sets, allowing Computer Vision models to detect micro-fractures with precision exceeding human capabilities.
Root Cause Analysis Agents – Unifying disparate data silos by connecting maintenance tickets with real-time sensor logs creates a semantic layer, allowing AI to correlate historical anomalies with current failure patterns to predict breakdowns before they occur.
Engineering: Building Smarter, Safer Infrastructure
Project Institutional Memory – Aggregating millions of archived emails, site reports, and change orders into a secure semantic layer allows Project Managers to query past delays and avoid repeating costly mistakes on future builds.
Generative Design Readiness – Cleaning and attributing metadata to historical CAD files and blueprints transforms static archives into high-quality datasets, fueling Generative AI tools to auto-optimize new floor plans and structural designs.
Aviation & MRO (Maintenance, Repair, Operations)
Intelligent MRO Manuals – Processing complex technical documentation to extract tables and diagrams into a structured format preserves context, ensuring mechanics receive precise, citation-backed answers that maintain 100% regulatory compliance.
Predictive Fleet Health – Normalizing decades of historical flight logs and pilot notes enables AI models to identify non-obvious safety patterns across the fleet, allowing for preemptive maintenance that prevents expensive Aircraft on Ground (AOG) events.
Automotive R&D & Supply Chain
Telemetry-to-Text Contextualization – Aligning massive streams of vehicle sensor data (CAN bus) with engineering bug reports creates a unified search layer, enabling engineers to query complex test scenarios using natural language and significantly accelerating vehicle validation cycles.
Supply Chain Intelligence – Restructuring messy, non-standardized supplier contracts and invoices into a unified Knowledge Graph empowers AI Agents to autonomously flag risks and price discrepancies across thousands of global vendors.
Advanced Manufacturing
Operator Copilots – Ingesting and structuring thousands of pages of technical manuals and shift logs into a vector-ready knowledge base enables AI assistants to instantly guide operators through complex repairs, reducing diagnostic time by up to 40%.
Visual Quality AI – Building pipelines to label and standardize raw video feeds from assembly lines creates high-fidelity training sets, allowing Computer Vision models to detect micro-fractures with precision exceeding human capabilities.
Root Cause Analysis Agents – Unifying disparate data silos by connecting maintenance tickets with real-time sensor logs creates a semantic layer, allowing AI to correlate historical anomalies with current failure patterns to predict breakdowns before they occur.
Engineering: Building Smarter, Safer Infrastructure
Project Institutional Memory – Aggregating millions of archived emails, site reports, and change orders into a secure semantic layer allows Project Managers to query past delays and avoid repeating costly mistakes on future builds.
Generative Design Readiness – Cleaning and attributing metadata to historical CAD files and blueprints transforms static archives into high-quality datasets, fueling Generative AI tools to auto-optimize new floor plans and structural designs.
The Business Value of AI-Ready Data
Accelerated Time-to-Value
A reusable data foundation moves AI from pilot to production speed, enabling the rapid deployment of multiple use cases and allowing the business to swap AI models seamlessly without rebuilding infrastructure.
Trusted Decision-Making
Grounding AI in verified, structured data eliminates “hallucinations,” ensuring critical business decisions are based on traceable corporate facts rather than statistical guesses, which is essential for user adoption.
Sustainable ROI
High-quality data preparation drastically lowers compute and token costs by filtering noise upstream, preventing the “Garbage In, Garbage Out” cycle that causes 85% of AI projects to fail.
Where does my organization stand with data?
Level 1: Fragmented (The “Swamp”)
State: Data is trapped in silos (Excel, PDFs, legacy SQL). No single source of truth.
AI Capability: None. Chatbots fail immediately.
Risk: High. Compounding technical debt and security risks.
Level 2: Analytics-Ready (The “Warehouse”)
State: Structured data is centralized in a Data Warehouse (e.g., Snowflake) for BI dashboards.
AI Capability: Basic. Can support simple forecasting but struggles with unstructured text or GenAI.
The Trap: Most organizations stop here, thinking they are ready. They are not.
Level 3: AI-Ready (The “Semantic Layer”)
State: Unstructured data (docs, emails) is vectorized. A semantic layer gives context to raw numbers.
AI Capability: Copilots. RAG systems can answer questions accurately. Employees trust the tools.
Value: 40-60% efficiency gains in knowledge work.
Level 4: Agentic (The “Autonomous Future”)
State: Data is accessible via APIs. Systems can “act” (read, reason, and execute tasks) without human hand-holding.
AI Capability: Autonomous Agents. AI manages supply chains, customer service, and diagnostics independently.
Value: Industry leadership and true competitive moat.
Most companies are stuck at Level 2. To succeed with AI Agents, you must bridge the gap to Level 3.
What our clients say
I am impressed with their openness to new solutions and dedication to making the tool effective and easy to maintain.
Narayana Pappu CEO – Zendata
Addepto delivered a platform with AI models that resulted in time savings. The team addressed the client's core problem and went above and beyond to understand the client's tech stack. Their technological expertise and state-of-the-art technologies complemented their business-oriented approach.
Michelle Medeiros Sr Director of Data & ML – Western Governors University
They didn't just "do requirements", they investigated our needs and advised on the best processes to achieve our objectives. They were mindful of costs and gave suggestions that would be great long term solutions. But most of all, they felt like a part of our team!
CEO of SimpleCater
Communication with the Addepto team was easy and fast. Continuous monitoring and measurement of our workflow helped to identify areas for improvement. Their approach from the start allowed us to improve the product taking into account all our requirements. The obtained result fully satisfied our company.
CPO of KeyedIn Solutions
AI Data Readiness: Executive FAQ
Does preparing our data now help us if we switch AI models next year?
Yes. It creates “Vendor Independence.” The AI model market is volatile (e.g., OpenAI vs. Google vs. Open Source). By building a robust, model-agnostic data layer, you own the “fuel.” If you need to swap the engine (the AI model) for a cheaper or better one in 2026, you can do so without rebuilding your entire application. Your investment in data preparation protects you from vendor lock-in and future-proofs your AI infrastructure.
What’s your typical engagement process, and how do we know we’ll see value before making a large commitment?
We follow a phased, low-risk approach that proves value before requiring large investments.
Phase 1 is Discovery & AI Data Readiness Assessment—workshops with your teams to inventory data sources, assess quality gaps, and identify your top AI use cases, outputting a maturity baseline and prioritized roadmap.
Phase 2 is a Proof-of-Value Pilot where we prepare data for your highest-impact use case using your real data, demonstrating how a prepared foundation accelerates time-to-value.
Phase 3 (if the pilot succeeds) scales to the full roadmap, building broader infrastructure, reusable pipelines, governance, and DataOps to support multiple initiatives. This means you reduce risk by validating assumptions with real data, budget is justified by demonstrated impact, and you’re never locked into large contracts based on theory.
Our data is trapped in PDFs, legacy databases, and scattered across multiple systems. Is it even realistic to make this AI-ready
Yes, and this is actually where most organizations start. 80% of enterprise value is currently locked in unstructured formats like PDFs, images, emails, and legacy SQL databases. Modern data engineering uses “Unstructured Data Pipelines” to extract text from PDFs, images, and emails, converting them into formats AI can understand (JSON/Vector).
We specialize in connecting disparate data sources—whether cloud, on-premise, or hybrid—and creating unified data layers that give AI systems coherent access to your entire information ecosystem. The key is not having perfect data upfront, but building the infrastructure to systematically improve and integrate it over time.
Will employees actually adopt AI tools, or will this be another technology that sits unused?
Employee adoption hinges entirely on trust, which comes from accuracy on day one. Employees abandon tools that lie to them or provide irrelevant answers. When AI is grounded in high-quality, prepared data from their actual work documents, employees are 2.6x more likely to adopt and champion these tools. Our approach ensures the AI gives relevant, accurate answers from the start by using properly prepared data that reflects real business context.
Additionally, by reducing the time your teams spend searching for data by 40–60%, AI becomes a genuine productivity multiplier rather than another system to learn. The goal isn’t just to deploy AI—it’s to make it indispensable to how your people work.
Does ‘preparing data’ require huge manual effort from my team, and will it disrupt current operations?
Only initially, and we minimize disruption. We need Subject Matter Experts (SMEs) to validate the “Golden Standard”—what good data looks like for your specific use cases. This typically involves focused workshops and validation sessions rather than months of full-time commitment. After establishing these standards, we automate the pipeline using modern DataOps practices. The preparation work happens in parallel with your ongoing operations, not as a replacement for them.
Our phased approach means we start with one high-impact use case, prove value quickly, then expand—so you’re never pulling entire teams away from critical work. The upfront investment of SME time pays dividends as automation takes over and subsequent AI initiatives launch without repeating the same data work.
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