--- title: "Data Engineering Services for AI Built on Your Tools | Nexum" description: "Data engineering services that get your data clean and ready for AI. We build the pipelines, storage and quality checks on the tools you already use." url: https://nexumautomations.com/services/data-engineering updated: 2026-10-31 publisher: Nexum Automations, an AI consulting firm --- > Nexum Automations is an AI consulting firm that builds what it recommends. It finds where AI pays off in a company, builds it on the tools the team already uses and stays until it is live and working. Data work for AI # Data engineering services built for AI on your current tools. Data engineering services for companies whose data sits in separate systems. We build the pipelines, storage and quality checks that bring it into one clean place for reports and AI, on tools you already use. 30 minutes. You leave knowing which 3 processes to automate first and what they cost you today. Trusted by companies worldwide **14-21 days** to your first live system **600+** processes automated **100+** hours returned to client teams every month Results ## Clean data behind systems that already run. Two real builds where connected data made the AI work. 1. SaaS / Software ### 85% of support tickets resolved by AI. A SaaS company with 50,000+ users had its best agents answering the same questions all day, and customers waited two hours for a reply. We built an AI assistant trained on their past tickets that answers instantly and hands complex cases to a person with full context. **1,200+** agent hours back every month **<5 s** first response, down from 2 h **+45%** customer satisfaction 2. Legal services ### Manual invoice entry eliminated. A boutique law firm's paralegals spent a quarter of their time retyping invoices, so billing ran late. We built an AI assistant that reads every invoice, matches it to the right case in Clio and syncs it to QuickBooks. **40+** hours back every month **99.8%** data accuracy **50%** faster billing cycle [See all case studies](https://nexumautomations.com/case-studies) Our data ## What this work costs by hand An estimate for one person who spends 10 hours a week on this work. Change the hours to match your team. | | Estimate | | ----------------------------------- | ----------------------------- | | Median annual wage, data scientists | $120,230 (May 2025) | | Median hourly wage | $57.80 (annual / 2,080 hours) | | Hours a week on this work | 10 (example input) | | Estimated yearly cost per person | $30,058 (hourly x 10 x 52) | Estimated, wages only (no benefits). Wage from the BLS Occupational Outlook Handbook, May 2025: https://www.bls.gov/ooh/math/data-scientists.htm. The hours are an example input, not a measurement. The problem ## AI work stalls on messy data. 80% of one agentic AI research project went to data engineering, alignment, governance and workflow integration. 52% of surveyed professionals named data quality and availability as their biggest AI adoption challenge. [MIT Sloan, one clinical-notes AI agent project; AI, Data & Analytics Network on the PEX Report, 2025 ↗](https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained) Most AI projects do not fail on the model. They slow down because the data lives in separate systems, in different formats, and nobody trusts it. * The same client appears three ways in three systems. One matched client record that every report and AI tool reads. * Someone copies numbers between systems by hand every week. Pipelines move the data on a schedule, with no copy and paste. * Errors surface after a wrong report or a wrong AI answer. Quality checks catch bad rows before they reach anyone. * Each new AI idea starts with weeks of data cleanup. AI-ready tables are waiting, so the next idea starts on day one. AI can only work as well as the data under it. Is it time? ## Signs your data needs engineering. Signals you need this * Reports take days because someone pulls data by hand * Two systems give two different answers to the same question * An AI idea stalled because the data was not ready * Nobody on staff owns how data moves between tools * You want AI on your own data, not a generic tool When not to invest yet * Clean data and working AI are expected within days * You want the lowest price more than a build that pays back * Only a data audit or a report is wanted, with nothing built * You cannot open up your tools or sign off on each step What you get ## What our data engineering services include. * A map of where your data lives, how it moves today and what the manual matching costs * Connections between your current systems, so records match across sources: the [data integration](https://nexumautomations.com/glossary/data-integration) layer * Data pipeline development services: scheduled pipelines that extract, load and prepare data without manual copying * A data warehouse, or the right simpler store, set up as one [single source of truth](https://nexumautomations.com/glossary/single-source-of-truth) for your company * Data architecture design: how data flows, who can read it and how it is checked, in plain documentation * Quality checks that stop duplicates, gaps and bad formats before reports or AI use the data * AI-ready tables wired into the assistants and agents built through [AI integration](https://nexumautomations.com/services/ai-integration) * You own what we build from day one, including the code, accounts, prompts and data How it works ## The Payback Loop. Five steps. Each build pays for the next. We don't start with the AI. We start with the money. One team of AI automation specialists runs all five 1. ### Map Consulting First, we find the manual work in your company and how many hours each task eats every week. Process map 2. ### Rank Consulting Each process gets a dollar figure and a rank by payback. You see what to automate first, and what to leave alone. AI roadmap, ranked by payback 3. ### Build Implementation We build the first system on the tools you already use. Nothing new for your team to learn. A working system 4. ### Prove Implementation Every system is tested end to end before launch. It doesn't go live until it works without errors. Tested, system live 5. ### Repeat Support The first build pays back fastest, so its savings fund the next one. Back to the map, and we keep every system running. Monthly support (optional) ↺ Back to Map Consultancies stop at the roadmap. Freelancers build without one. The loop only works when one team runs all five. You approve each step; we do the work. Your options ## Why not a big consultancy, a freelancer, or a new hire? vs Big consultancy vs Freelancer vs In-house hire | Nexum (done for you) | Big consultancy | Freelancer / no-code builder | In-house AI hire | | | -------------------------------------- | ----------------------- | --------------------------------- | ---------------------- | ----------------------------- | | Tells you what to automate first | ✓ | ✓ | Rarely | Sometimes | | Builds it on the tools you already use | ✓ | Usually hands off to another team | ✓ | ✓ | | Price fixed before work starts | ✓ | Rarely | Sometimes | A salary, not a project | | Time to first live system | 14-21 days | Months | Varies | Months to hire, then build | | You own everything built | ✓ | ✓ | Not always | ✓ | | Time from you, per week | \~1 hr review | Steering meetings | You manage the project | You manage the person | | Typical cost | Fixed price per project | Six-figure engagements | Hourly, open-ended | A full-time salary + benefits | Based on how each option typically works. If another option fits you better, we'll tell you on the call. FAQ ## Questions about data engineering services. * ### What are data engineering services? Data engineering is the work of building pipelines, storage and quality checks that move data from separate systems into one clean place for reports and AI. Data engineering service providers handle that build; ours is set up on the tools a company already uses. * ### How is data engineering consulting different from data integration consulting? Data engineering consulting services cover the whole data foundation. Data integration consulting is one part: connecting systems so records match. We handle both in one engagement, so the connections and the storage behind them are designed together. See [context engineering](https://nexumautomations.com/glossary/context-engineering). * ### Do I need a data warehouse before starting AI? Not necessarily. Data warehouse consulting starts by asking what your AI and reports actually need. Some companies need a full warehouse. Others only need a few connected tables. We size the store to the job rather than defaulting to the biggest option. See what a [data warehouse](https://nexumautomations.com/glossary/data-warehouse) is. * ### What does data architecture consulting cover? Data architecture consulting decides how data flows between systems, where it is stored, who can access it and how its quality is checked. On our builds this is documented in plain language, so your team understands it and can keep it running. * ### How does the data feed my AI? The pipelines load cleaned data into AI-ready tables. Assistants and agents then read from those tables instead of from messy sources. That is the core of data engineering for AI: the model gets consistent, matched, checked data to work from. * ### ETL or ELT: which one fits my systems? Both ways move data from a source system into storage. ETL cleans and reshapes the data before loading it. ELT loads it first and reshapes it in the destination. We pick the order that fits your systems, including older ones with limited processing. * ### How much of an AI project is the data work? In one MIT Sloan research project, an AI agent reading clinical notes, about 80% of the effort went to data engineering, stakeholder alignment, governance and workflow integration together, not to the model. The split varies by project, but the data work is rarely small. * ### Who owns the pipelines and the data? You do. The code, accounts, prompts and data are all yours from day one. Optional monthly support can keep the pipelines running and add the next data source to your roadmap. Read about [context engineering vs prompt engineering](https://nexumautomations.com/compare/context-engineering-vs-prompt-engineering). * ### How long does a data engineering project take? Expect 1 to 2 weeks for the roadmap phase. The first working system usually goes live within 14 to 21 days, and a full build runs 6 to 8 weeks. A fixed-price proposal sets the exact scope and timeline before anything starts. --- Source: https://nexumautomations.com/services/data-engineering Free AI diagnosis (30-minute call): https://nexumautomations.com/#book - Services: https://nexumautomations.com/services - Guides: https://nexumautomations.com/guides