AI Development & Consulting Services
End-to-End AI Development & Consulting Services That Ship to Production
Most AI initiatives stall in the "lab" phase. STX Next builds solutions tailored to production reality. Whether you need a secure, internal AI workspace in two weeks or a complex predictive maintenance system for a regulated industry, we deliver fixed-price, high-certainty solutions backed by 1,000+ projects and 20 years of engineering experience.
STX Next’s impact in numbers
Our services
Respecting your budget means starting small, proving value, and scaling only what is proven to work. We don't ask for a leap of faith. We prove results on your actual data in weeks, not months. 100% of code ownership remains with you, meaning no vendor lock-in, with all deliverables sent straight to your repository.
1. AI Agents & Agentic Systems
You do not need another chatbot. You need software that monitors your systems, reads incoming data, decides what to do, and acts: creating tickets, sending alerts, updating records.
A 4-week fixed-price MVP, then T&M for additional capability. Built on LangChain, LangGraph, MCP, AWS Bedrock, OpenAI, Claude, or Gemini.
An IT operations agent that correlates Dynatrace logs with ServiceNow tickets to deliver a root-cause hypothesis in real time. A training-script generator that drafts call-centre scripts for every product or process change.
2. Agentic AI Workspace
Your teams want a secure version of ChatGPT that knows your documents, SharePoint, GDrive, CRM, and Confluence. Public AI tools are off-limits because of data residency.
A self-hosted AI operational space with a RAG knowledge base, department-level workspaces, task agents, and workflow automation. Architecture compatible with GDPR, the EU AI Act, and DORA, where those apply to you. A functional workspace built on your data in 2 weeks. Full production rollout with compliance audit scoped separately.
95% faster document search (45 minutes down to 2 minutes). 15 hours per team are saved each week in HR, Marketing, and Sales.
3. RAG Development for Enterprise Documents
You have specific documents (contracts, regulations, technical manuals) and need an objective answer: will RAG actually work on this content, or not?
A fixed-price, 2 to 4-week proof of concept on your real data. You get a working prototype, a quality benchmark report with named failure cases, and an honest go-or-no-go recommendation. Stack: Python, FastAPI, AWS Bedrock, pgvector or ElasticSearch, Docling, OpenAI or Claude.
Linde GmbH (RAG with RBAC and Haystack), Whitespace Global (RAG report generation on self-hosted LLMs).
4. OCR & Document Intelligence
You process PDFs, scans, claims forms, or KYC documents at scale, and data sovereignty rules say documents cannot leave your infrastructure.
A Kubernetes-based OCR service with LLM post-processing. Three deployment modes: full on-prem, hybrid (raw documents stay inside, only anonymised text is sent to the cloud), or fully managed. Configurable extraction prompts, ready I/O connectors. 1 to 2 weeks to customise.
Banking (KYC), insurance (claims and forms), manufacturing (technical scans), public administration (digitisation).
5. Quick Prediction Sprint
You have historical data and a question: can we predict churn, demand, fraud, or failure? You need an answer your board can act on.
A 3-week fixed-price classic ML PoC. We compare 2 or 3 models against a baseline, run SHAP and LIME explainability, and hand over an experimental notebook plus a board-ready slide deck with a clear go-or-no-go recommendation. Classification, regression, and time-series forecasting are all covered.
6. Custom AI/ML Software Development
Your problem does not fit a fixed-price box. You need a dedicated team that understands process physics, financial regulation, or domain semantics to develop a custom-made AI/ML platform just for you.
A senior, blended team (data engineers, ML engineers, MLOps, product designers) embedded with yours. Eight areas of expertise covered in one team, from Enterprise RAG through Computer Vision to MLOps. 2 to 12 months. Individual valuation.
Industries we specialize in
STX Next specializes in regulated sectors where AI accuracy and regulatory compliance are non-negotiable.
Finance
Audit trails and KYC processing leave no room for hallucination. We deliver precision through RAG assistants that navigate 100 to 200 internal regulations to support your advisors, utilizing GDPR-compliant CloudFerro infrastructure when required. To maintain your highest document standards, we build in custom features like deduplication and discrepancy search.
Complete Policy Coverage
Our RAG assistants cover extensive policy and regulation libraries for both advisors and underwriters.
Data Sovereignty
Deploy on-premise or hybrid OCR for KYC document extraction to maintain total data control.
Explainable Risk Modeling
We deploy credit scoring and fraud detection models backed by SHAP explainability.
Audited Workflows
Our AI advisor co-pilots draft client responses, populate compliance forms, and cross-check recommendations against your latest internal policy.
See our finance solutions
Insurance
If your adjusters rely on memory and manual emails, workflows for claims, policies, and forms will stall. We unify your disconnected policy databases and claims documents into a single AI workflow that triages cases, scores risk, and prices accurately.
Knowledge at Your Fingertips
Give your underwriters and adjusters instant access to comprehensive policy and procedure knowledge bases.
Structured Data Extraction
The system processes claims documents to extract clean, structured outputs like line items, dates, and amounts.
Auditable Insights
Our fraud scoring and pricing models deliver clear, trackable feature contributions for full transparency.
See our Insurance solutions
Oil & Gas, Energy
Success in heavy industry depends on an understanding of process physics. A model that fails to grasp the mechanics of an Olefin furnace cannot reliably predict its failure. Our engineering teams collaborate directly with your domain experts to model physics-based features, time-series anomalies, and trading signals from billions of records.
Predictive maintenance scales across hundreds of TB of IoT and time-series data.
Development of an analytics platform to optimize trading strategies in energy and gas markets.
Operational procedures and regulatory documentation are managed through secure RAG implementations.
See our energy solutions
Industrials & Manufacturing
You likely generate massive volumes of sensor and document data, but leave them underutilized. We help you transform this data into shorter downtime windows and smarter production scheduling while eliminating manual PDF processing.
Physics-Backed Maintenance & Vision QC
Our predictive maintenance systems utilize physics-based features and SHAP explainability, combining them with real-time computer vision to detect microscopic defects and cracks directly on the production line.
Dynamic Shop-Floor Scheduling
Scheduling assistants equip your production planners with high-probability countdown views, enabling intelligent shop-floor rescheduling to automatically manage unexpected operational disruptions.
Automated Supply Chains
Our AI-powered OCR engine reconciles PDF extraction with your order data, instantly capturing critical parameters from invoices, purchase orders, and delivery notes.
Autonomous Workflow Routing
The system routes Engineering Change Orders (ECOs) and Non-Conformance Reports (NCRs) instantly across your teams, automatically flagging exactly which BOMs and drawings are affected.
See our Industrials & Manufacturing solutions
Healthcare
Your clinicians cannot manage rapidly changing medical guidance by memory alone; our clinical knowledge hubs solve this exact challenge. We build medical document extraction and risk-scoring models designed to respect your strict data residency and audit requirements.
Protocol-Driven RAG
We build clinical knowledge RAG systems directly over your official guidelines and protocols.
Flexible Deployment
Deploy medical document and form processing on-premise whenever your security compliance requires it.
Smart Patient Triage
AI agents triage incoming patient inquiries and route cases directly to the correct clinical workflow.
AdTech & MarTech
Manual reviews of campaign materials rarely scale at the pace of modern brand growth. We implement computer vision, classification, and recommendation systems to remove manual review from your critical path.
Automated Brand Protection
Our brand assurance and creative QA systems utilize computer vision for precise logo, font, and object detection.
Sponsorship Tracking
We automate sponsorship visibility analysis through advanced, real-time logo detection.
Operational Efficiency
Boost your operational metrics using campaign automation agents and high-accuracy lead scoring.
See our AdTech & MarTech solutions
STX Next AI/ML competence mapping
A production AI system is rarely one thing. An enterprise RAG implementation needs document processing, a reliable retrieval layer, access controls, and an audit trail. An agentic system needs the reasoning model, the tool integrations, and the monitoring to catch when it acts on bad input. Our teams have practical expertise across eight competence areas because real AI projects draw on them simultaneously.
Expertise Built On +50 AI/ML Projects
Our teams help global corporations adopt AI solutions responsibly, securely, and cost-effectively. How do we do it? Let our work speak for itself.
20% Reduction in Unplanned Downtime with Four ML/AI Projects in Two Years
A global leader in plastics, chemicals, and refining needed to turn massive sensor and operational data into shorter downtime, smarter scheduling, and faster supplier processes. Our expert team delivered four production ML/AI builds in two years, covering predictive maintenance on Olefin furnaces, a furnace scheduling assistant, automated supplier-schedule processing, and DAM/RTM price prediction for ERCOT energy trading. As a result, the client cut unplanned downtime by 20% and now handles hundreds of TB of data and billions of records from two factories.
US
27% Better Search Quality and 3.5% Higher Conversion for a Leading Podcast Platform
Podimo, a Danish podcast and audiobook platform, needed a search engine that understood query meaning, not just keywords, across an 11M+ audio library. Our expert team built a semantic vector store with ElasticSearch and BigTable, generated and indexed audio embeddings in real time through Pub/Sub, and layered a learning-to-rank model trained on user interactions plus a conversational search assistant on top. As a result, Podimo lifted search quality by 27%, conversion by 3.5%, and meaningful listens by 1.3%, with a pipeline that ingests and indexes new audio in near real time.
Denmark
Brand QA at Campaign Scale: 4 Hours of Manual Review Cut to 15 Minutes
Wunderman Thompson's Brand Guardian platform needed to scale automated content quality assurance across global campaigns, checking brand consistency, inclusivity, compliance, and creative standards on every asset. Our expert team rebuilt the platform on a Kubernetes-based ML microservices architecture with auto-scaling, Celery workers, and computer vision pipelines for logo, font, and object detection, backed by a messaging system for high-volume task management. As a result, asset processing dropped from 4 hours to 15 minutes, the architecture absorbs 100% volume growth, and new client onboarding moves faster on a production-grade ML foundation.
Global
What Sets Us Apart
Companies in banking, insurance, healthcare, energy, and manufacturing carry more risk than most. The AI vendor needs to ship working code, respect data residency, and prove value before the full budget is committed.
Complete pricing ladder
Fixed prices start at PoC and scale up to enterprise builds. This modular path allows you to start small, see value, and scale only what works, with first production value in 2 to 4 weeks.
Real metrics, not estimates
Metrics from our deployments: 20% reduction in unplanned downtime, a 27% search quality lift, content QA cut from 4 hours to 15 minutes, and 88% F1 logo detection accuracy.
Deep sector experience in manufacturing & energy
+20 ML/AI projects backed by in-house domain experts with a deep understanding of process physics beyond the code.
Agentic AI shipped, not pitched
Production-grade agent systems, including intelligent IT operations tools that correlate real-time system logs with open tickets, automated training script generators for global operations, and secure, self-hosted workspaces.
Trusted by enterprise leaders
Our secure production systems support market leaders globally, including Man Group, Wayfair, Mastercard, ESA, Decathlon, Canon, Google, and Linde.
OCR and LLM with full data sovereignty
Three flexible deployment modes to protect your data: full on-premise, where data never leaves your infrastructure, hybrid, where only anonymized text leaves the perimeter, or fully managed cloud.
By the numbers
Certifications & Partnerships
ISO/IEC 27001 certified information security
AWS Advanced Tier Services Partner
Snowflake Services Partner
Databricks BrickBuilder Partner
Microsoft technology stack experience
n8n partner
What our clients say about us
Even though we believe that our work speaks for itself, we are always grateful for words of appreciation from our clients.
Client
testimonial
They have consistently delivered high-quality ML models, improved our search engine, and successfully prototyped applications with LLMs.
Podimo
Client
testimonial
The system allowed us to validate the RAG concept on our documentation within 4 weeks and make an informed decision about further investments.
Client
testimonial
Great skill, craft, and agile thinking in the way they go about their work.
Verve
Let's talk
Schedule a chat with our AI Director and one of our senior engineers to discuss your AI development needs.
FAQ
How long does AI implementation take?
It depends on the entry point. An Agentic AI Workspace goes live in 2 weeks. A RAG PoC on your documents runs 2 to 4 weeks. A Quick Prediction Sprint is 3 weeks. An AI Agent MVP is 4 weeks. Full enterprise RAG and custom builds run 10 weeks and up, depending on scope and integrations.
How much does AI development cost?
Entry-point pricing is transparent and fixed. The point of starting with a fixed price is to give you a working artefact and a real decision before you commit to a larger budget. If you're interested in a specific solution, please contact us for a detailed quote!
What is an AI agent, and how is it different from a chatbot?
A chatbot answers questions. An AI agent reads incoming data, decides what to do against defined rules, and takes action across your systems: creating tickets, sending alerts, updating records, with or without a human in the loop. Our AI Agent MVP gives you a working end-to-end agent in 4 weeks, not a slideshow.
What is RAG, and how do you know it will work on our documents?
RAG (Retrieval-Augmented Generation) is a pattern where a language model answers questions grounded in your documents instead of its training data. The honest answer is that RAG quality depends entirely on your documents. That is why we run a 2 to 4 week fixed-price RAG Mini PoC on your real content, benchmark answer accuracy with named failure cases, and give you an objective go or no-go recommendation before any production commitment.
Do you offer on-premise or hybrid LLM deployment?
Yes. Our OCR & Document Intelligence service runs in three modes: full on-prem (data never leaves your infrastructure), hybrid (raw documents stay inside, only anonymised text is sent to cloud LLMs), or fully managed. This is built for banking, insurance, healthcare, and public administration, where data sovereignty is non-negotiable.
What is the difference between AI consulting and AI development services?
Consulting helps you decide what to build. Development builds it. We do both, and we deliberately blend them: every engagement starts with a discovery and a fixed-price PoC that answers feasibility before any full build. You get strategy, code, and a working prototype from the same team. No vendor handoffs.
Which industries do you specialise in?
We have shipped AI references in banking and finance, insurance, manufacturing, oil and gas, energy, healthcare, AdTech and MarTech.
What does your AI development process look like?
Five steps. Discovery and AI readiness assessment (up to 3 sessions, free entry point). Architecture and data review with a written assessment. Fixed-price PoC on your real data with a benchmark report. Production build, Scrum-based, with CI/CD and full code ownership delivered to your repo. Handoff, MLOps, and optional ongoing support. First production value typically lands in 2 to 4 weeks regardless of which path you start on.
