Strategy + Engineering
We start with the problem and ROI case, then build the model and ship it. Same team across both phases, no analyst-to-engineer handoff.
Enterprise AI & Machine Learning Services
Things at Web builds and deploys enterprise AI systems predictive models, NLP pipelines, computer vision, and LLM-powered applications that integrate with your existing infrastructure and deliver measurable ROI. Production deployments measured in business KPIs, not proof-of-concept decks. Bias testing and explainability on every model.
Enterprise AI and machine learning services are end-to-end engagements that take a business problem, identify the right AI approach, build and train the model, and deploy it into production where it runs reliably at scale. The result is an automated decision system that improves with data rather than requiring constant human intervention.
Enterprise Intelligence
Enterprise AI & Machine Learning Services. From Proof of Concept to Production Revenue Ship.
Overview
Things at Web builds and deploys enterprise AI systems predictive models, NLP pipelines, computer vision, and LLM-powered applications that integrate with your existing infrastructure and deliver measurable ROI. Production deployments measured in business KPIs, not proof-of-concept decks. Bias testing and explainability on every model.
Service Overview
What we actually deliver
Enterprise AI and machine learning services are end-to-end engagements that take a business problem, identify the right AI approach, build and train the model, and deploy it into production where it runs reliably at scale. The result is an automated decision system that improves with data rather than requiring constant human intervention.
Our AI engagements bridge ML engineering with deep experience in manufacturing, retail, financial services, and healthcare. Successful AI is more than the model it is alignment of capability with the business decision the model is meant to support.
Why choose us
Five reasons to choose us
MLOps from Day One
Every production deployment ships with monitoring for accuracy, latency, and data drift plus automated retraining triggers.
Bias + Explainability
SHAP/LIME on every prediction, fairness metrics across demographic subgroups, documented bias assessment before mainnet.
Integrates With What You Have
AI delivered as API-first services. Your CRM, ERP, and data warehouse consume it without rebuilds.
RAG Where It Belongs
LLMs grounded in your private knowledge base, not training data. Domain-specific answers without hallucination risk.
What We Build
Our capabilities
The core capabilities we deliver in every engagement all strict-fact, all in production today.
AI Strategy & Readiness
We audit your data, identify the 3-5 highest-ROI use cases, and build a 12-month roadmap.
Predictive Analytics
Demand forecasting, churn prediction, revenue modelling using gradient boosting and time-series models.
Natural Language Processing
Document classification, entity extraction, sentiment analysis, and chatbot pipelines on transformer architectures.
Computer Vision
Quality inspection, object detection, OCR, and visual search deployed on edge devices or cloud APIs.
LLM Integration & RAG
GPT-4, Claude, Gemini grounded in your private knowledge base. AI answers from your data, not training data.
MLOps & Lifecycle
Automated retraining, model versioning, drift monitoring, and A/B testing so models don't degrade silently.
The process
How we work
Our step-by-step engagement model ensures complete transparency, predictable iterations, and full code ownership on handover.
Discovery & scoping
We map your goals, audit anything that already exists, and agree on scope and success measures before any code is written.
Plan & team
You get a concrete plan with timeline and a named delivery team the same specialists who will actually build your product.
Build in iterations
Working software every iteration, with direct access to the engineers. No account-manager relay, no outsourcing chain.
Launch & ownership
Full ownership of code, hosting, and design files transfers to you on handover. Ongoing operations support is optional, never lock-in.
FAQ
Frequently Asked Questions
Answers to common questions about our process, deliverables, and how we work.
AI consulting defines the strategy which problems to solve, which data you need, what ROI is realistic. AI development services build and deploy the actual systems. Things at Web does both: we start every engagement with strategy to build the right thing, then move directly into development.
It depends on the problem. Classification and regression models often perform well with 10,000–50,000 labelled examples. Time-series forecasting needs 2–3 years of historical data. For LLM applications using RAG, you can start with a few hundred well-structured documents. We assess your data position in phase one.
A well-scoped predictive model can go from kickoff to production in 8–16 weeks. NLP or computer vision with custom labelling typically takes 16–24 weeks. LLM-based applications with RAG can often be deployed in 4–8 weeks because they leverage pretrained foundation models. Timeline depends on data readiness.
Yes. We build AI as API-first services that integrate with your CRM, ERP, data warehouse, or custom applications via REST or gRPC. Your existing systems don't need to be rebuilt they consume the AI output through a well-defined interface.
Four layers: data auditing to catch biased training sets, statistical fairness metrics evaluated across demographic subgroups, explainability tools (SHAP, LIME) so every prediction is interpretable, and human-in-the-loop review for high-stakes decisions. No model goes to production without documented bias assessment.
Retrieval-Augmented Generation (RAG) is an architecture where an LLM answers questions by first searching your private knowledge base and using retrieved documents as context. AI answers from your actual policies and data not from training data alone. Eliminates hallucination risk for domain-specific queries.
Yes. Production deployments include a monitoring dashboard for accuracy, latency, and data drift. We offer managed MLOps retainers covering automated retraining triggers, alert escalation, and quarterly performance reviews. Models that aren't maintained degrade we build infrastructure to ensure they improve.
Get Started
Ready to talk Enterprise AI & Machine Learning Services?
Book a 30-minute discovery call. We'll walk through your needs, propose a concrete first wave, and tell you honestly whether it's the right fit.