Systems that turn data into decisions.
Nodevex builds AI and data systems that hold up in production — assistants and RAG over your own data, forecasting retrained on a schedule, the software they run in, and the growth work that follows. Everything ships documented, with the archive and the runbook. Most of it was built for clients we don't name.
AI systems & integrations
RAG over your own documents and data, assistants and agents scoped to one job, and AI added to the systems a business already runs — your CRM, your admin, your pipeline — rather than a second tool beside them.
Forecasting & analytics
Forecasting models, the dashboards that show their output, and the data archive they train on, retrained on a schedule.
Web data collection infrastructure
Structured feeds from sites that change their markup without warning. One suite covers 20+ platforms at 98%+ extraction success and alerts when a feed breaks.
Websites, web & mobile apps
Company sites, web apps and iOS and Android apps, with the internal tools behind them. 40+ applications in production, at 99.9% uptime.
E-commerce growth & paid acquisition
Paid acquisition toward the segments that already convert, lifecycle email instead of one broadcast list, and storefront changes that move average order value rather than traffic.
Design, motion & AI-assisted visuals
Product and marketing design with Pixxibird, our sister design studio, then the motion and the image and video generation that dress it — ComfyUI pipelines on open models, with the graph built for what the piece actually needs.
The full register · 35 capabilities
Public caseAsk usIntelligence
- RAG and document search: Retrieval over your documents, knowledge base or database, with an LLM answering from what it finds.
- Agents and assistants: Tool-using agents scoped to one job, with the boundaries and the audit trail decided up front.
- AI integration into existing systems: AI added to software a business already runs — extraction, classification, summaries, reports — behind a real endpoint, with evaluation and cost accounting, and no rebuild of what works.
- AI and machine learning: Models trained on your own data where a rule would not hold — classification, scoring, forecasting — with the evaluation that says whether it beat the rule.
- Trading systems: Model ensembles per instrument, retrained on a schedule against a versioned feature store.
- Prediction markets: Orderbook and gamma monitors that archive every observation, not just the interesting ones.
- Sports analytics: Matchup models over a multi-season archive, with odds collection running beside them.
- Business intelligence: Dashboards and scheduled reporting fed by the pipeline itself, so the number on screen has a lineage.
- Data engineering: Ingestion, stream processing and schema design for data that has to survive being queried a year later.
- Model review: An honest read on whether the model is worth building, before anyone commits a quarter to it.
Tech
- Websites: Company and product sites: fast, accessible, and editable by your own team after launch.
- Web apps: Full-stack web and SaaS products — the app, the admin behind it, and the deploy path.
- Mobile apps (iOS and Android): Apps for iOS and Android, with the API, sign-in and push notifications they depend on.
- Web data collection: Per-platform collectors against a shared schema, with confidence scoring and a review queue.
- Cloud and infrastructure: Servers, CI/CD, containers, monitoring and the alerting that catches a failure before a client does.
- DevOps and CI/CD: Build, test and deploy pipelines, infrastructure as code, and the release path a team can run without a person holding the rope.
- Blockchain and smart contracts: Contracts, wallet integrations and on-chain data pipelines, for products where the ledger is the point rather than the pitch.
- Process automation: The recurring internal work that currently runs on a person and a spreadsheet.
Digital
- E-commerce build: Storefronts built for average order value and repeat rate, not for the launch screenshot.
- Performance marketing: Paid acquisition reallocated toward the segments that already convert, with attribution that holds up.
- Lifecycle email: Welcome, browse, cart and post-purchase flows replacing the single broadcast list.
- Search and content: Technical SEO and the pages worth ranking, measured against revenue rather than impressions.
- Conversion optimization: Testing on the steps that carry the money, with the analytics wired before the first variant ships.
- Lead generation: Outbound and inbound routed into one pipeline, so a lead's origin is still readable at close.
Studio
- Architectural visualization: Photoreal stills, animation and virtual staging generated from the working model, not a marketing fork.
- BIM coordination: Revit modelling and clash detection logged against model elements, with 4D and 5D where the schedule needs it.
- Graphic design and content: Brand assets, campaign and social graphics, and the written content that goes with them — with Pixxibird, our sister design studio.
- UI/UX design: Interface and experience design for web apps, dashboards and sites, from wireframe to the screen the build follows — with Pixxibird, our sister design studio.
- Motion graphics: Animated explainers, product and interface motion, and cuts for social.
- Image and video generation: Stills and video loops made with generative tools, directed and graded to match the brand.
- 3D Rendering: Stills and animated renders from model to final frame, lit and staged for presentation.
- AR/VR Unreal Engine: Real-time scenes and walkthroughs built in Unreal Engine for AR, VR and interactive presentation.
- VFX: Compositing, cleanup and effects for video, from screen work to final grade.
- Structural engineering: Foundation, steel and concrete systems with seismic analysis in Robot and ETABS.
- MEP and sustainability: HVAC, electrical, plumbing and fire protection, with energy modelling and LEED documentation.
Step 01Start with a free auditSend the data, the tools and the decision you want to improve. You get back the shortest route to it and a price for the first phase — or the reason a system isn't worth building yet. No fee, and what the audit finds is yours either way.
Step 02Build the system, documented and yours to runPipelines, models and interface built as one thing, in your stack, with the evaluation written down as it goes rather than reconstructed at the end.
Step 03Hand over with the archive and the runbookThe data archive, the model versions and the operating notes transfer with the system. Nothing needed to run it stays on our side.
40+PRODUCTION APPLICATIONS
99.9%SYSTEM UPTIME
1,552TRAINED MODELS
2012BUILDING SYSTEMS SINCE