# Leading the AI Revolution. Velais brings real AI engineers inside your company as the fast, agile, skilled team you never had. ## People and Systems We train the people who will use AI first, and build the systems after. - [Corporate AI training](/ai-training): AI tools change every month, and even motivated teams struggle to keep up. Our four-day course on the Claude platform ends with each person building a skill for a task from their own job. It costs USD 750 per participant. - [AI systems](/ai-systems): Company data sits in the ERP, the CRM, email and spreadsheets, and the numbers rarely match. We get the data and processes ready, then build AI agents and tools that work inside the systems you already use. ## Our work ### Assistant on a technical catalogue A large industrial manufacturer with an extensive technical catalogue. Pillar: Systems. - 252 users in the first 10 weeks - 5,873 questions in the first 10 weeks - 1,800+ technical documents indexed **Problem.** After a merger, the sales reps were working from two separate sets of technical documents, more than 1,800 between them, with no shared way to find the right product code. Answering a customer meant digging through datasheets, then looking up price, stock and lead time in the ERP. The company's own team had already built a tool for this and rejected it as unreliable. **What we built:** - An assistant that answers questions in plain language with the right product code and the sources behind the answer - Price, stock, minimum order and lead time read from the ERP as the signed-in user - Search across more than 1,800 technical documents, combining keywords, vectors and reranking - An evaluation of correctness, faithfulness to sources and recall; every change is tested on 50 real questions before release - Hosting on Azure, with sign-in through the company's own accounts ### One set of KPIs, rebuilt from the ERP A mid-sized wholesale distributor running SAP Business One. Pillar: Systems. - 21 KPIs reconciled to the cent - 30 source tables read every night **Problem.** Sales, finance and logistics each took their numbers from a different tool and defined the same KPIs in their own way, so two reports could give two answers to the same question. 21 of the KPIs management needed were not visible to it at all. Putting AI on top of that would only have scaled the confusion. **What we built:** - KPIs on sales, receivables, stock and logistics, rebuilt every night from 30 raw source tables and read-only on the ERP - Every KPI built twice, independently, and released only when both versions matched to the cent - A dashboard and an API behind the company's own sign-in, with roles taken from the company's own groups - A read-only connector so people can ask Claude for the KPIs; it acts as the user who asks and refuses anything but reads ### Social posts turned into weekly trend signals A data company that sells trend signals to the music industry. Pillar: Systems. - 17.9M+ posts collected from 4 platforms - 103,676 artists tracked - $0.0001 AI cost per post - 54% lower cost per post at equal quality **Problem.** The client sells weekly trend signals to record labels, and its product ran on social data bought from an outside supplier. When the supplier cut off the feed, the product had nothing left to run on. It needed a pipeline of its own, able to read millions of posts across four platforms at a cost that stays low at that scale. **What we built:** - Collection from YouTube, Reddit, TikTok and Instagram in parallel, where one platform failing does not stop the others - Rules that match genres and keywords, and a model that reads each post only for artists named in free text - A cache per prompt version, so re-running millions of posts costs no new model calls - A metered API with keys and quotas per customer, and a dashboard - Twelve release gates on a 3,000-post test set, with a test proving the gates fail when something breaks ### Daily site reports drafted by an agent, under one set of AI rules A software company that runs field operations on large infrastructure projects. Pillar: Systems. - 1 enterprise agent connected to all 3 applications - 5 min for the daily report, from over an hour today (target) - 12 tools across 3 applications - ~100 acceptance cases, security tests included **Problem.** On large infrastructure projects, every site manager filled in the daily progress report by hand at the end of the day, a job that took more than an hour. Reports arrived late, incomplete or not at all. The company also wanted every AI client, its own agent as well as Claude or ChatGPT, to reach its data under the same access rules. **What we built:** - An agent that drafts the daily report from the site chat, by text or voice and in several languages, checks live master data and asks when in doubt; the manager reviews and submits - One governance layer, a remote MCP server, in front of every AI client: identity from the token, a policy checked on every call, and an allow-list on what each tool returns - Only the layer holds the API key, so a model that goes wrong cannot reach anything outside it - One access record per call, on Azure with Entra ID, API Management, Key Vault and Log Analytics - An evaluation built from past approved reports, in five levels of difficulty; the next level opens only at 100% right with nothing invented ## Talk to us Book a call with us.