AI automation that replaces real manual work
We build AI-powered systems that actually work in production — from LLM copilots and document processing to predictive analytics and workflow automation.
✓ routed to a senior engineer
✓ estimate en route
const build = [ // what we build
AI & automation services
GPT-4, Claude, and Gemini wired into your product — RAG pipelines and copilot features that ship.
Context-aware assistants with knowledge-base retrieval, not scripted decision trees.
OCR, extraction, classification, and data normalisation for the paperwork nobody wants to type.
ML models for forecasting, churn, and anomaly detection built on your own historical data.
Replace manual processes with event-driven automation triggered by real system events.
Image classification, object detection, and quality inspection at production throughput.
// technical depth — production AI, not demos
AI capabilities
RAG pipelines, vector stores, and agent orchestration — grounded in your actual documents.
Domain-specific model fine-tuning on your proprietary data, not generic prompts.
Azure Document Intelligence, AWS Textract, and custom OCR pipelines for messy real-world input.
PyTorch, scikit-learn, Hugging Face, and MLflow — the tools, used properly.
FastAPI, Triton, SageMaker, and Vertex AI inference — sized for real request volume.
MLflow experiment tracking, model versioning, and drift monitoring once it's live.
import { stack } from "@dww/ai" // the AI stack
AI tech stack
function aiProcess() { // click a stage to inspect
AI development process
We identify the specific manual task or decision to automate — and define what success looks like before touching a model.
- ✓Manual process mapped end to end
- ✓Success metrics agreed up front
- ✓Pilot scope, not a moonshot
We audit available data quality, quantity, and labelling needs — the honest bottleneck in most AI projects.
- ✓Data quality & coverage audit
- ✓Labelling & ground-truth plan
- ✓Privacy & compliance check
Baseline model, evaluation metrics, and iterative refinement — measured against the numbers agreed in step one.
- ✓Baseline model shipped fast
- ✓Evaluation harness against real data
- ✓Iterative refinement to target accuracy
Model serving, monitoring, and a feedback loop integrated into your product — not a notebook left running on someone's laptop.
- ✓Model serving in production
- ✓Drift & performance monitoring
- ✓Feedback loop for retraining
// selected AI builds — production systems
Systems we've built
Replaced a 3-person data entry team extracting fields from supplier invoices. GPT-4o plus a validation pipeline processes invoices daily, with a human review queue for low-confidence cases.
RAG pipeline over 10,000 legal contracts. Associates ask questions in plain English and get cited answers in seconds — replacing hours of manual search.
AI triage for a telehealth platform — classifying patient symptom descriptions into urgency tiers and routing to the correct specialist queue, replacing manual admin review.
// why DWW for AI
Production AI, not demos
We build AI features that work in production, not just demos that impress once.
Every AI project is defined with measurable success metrics before we write code.
On-premise or private cloud LLM options available for sensitive data.
Model monitoring, drift detection, and retraining pipelines — not a one-time handoff.
Digital Web Weaver is an AI automation and development company built around production, not demos. We define measurable success metrics before writing code, build RAG pipelines with LangChain, LlamaIndex, Pinecone, and pgvector, and integrate with your existing stack via FastAPI — including your SaaS, CRM, and ERP systems. Teams are distributed across India, the UK, South Africa, and the US, with a free discovery session available to scope what's worth automating.
// client words — AI builds
Our invoice processing used to take three people 6 hours daily. Now one person reviews edge cases the AI flags. Accuracy surpasses manual work.
What impressed me was their emphasis on measuring accuracy upfront. We defined success first, built a proof-of-concept, then proceeded with validated numbers.
The AI triage feature is now a core product differentiator. It integrates into every patient flow and accuracy keeps improving.
// ai-automation.faq.md
For well-defined, repetitive tasks — yes. We scope realistically: pilot one process, measure ROI, then expand.
Options include private cloud LLMs, on-premise Ollama, fine-tuning with your data on isolated infrastructure, or using only anonymised data.
Retrieval-Augmented Generation — an LLM answers questions by searching your documents first, so answers are grounded in your actual data.
Prototype with measurable results: 3–6 weeks. Production system with monitoring and feedback loop: 2–4 months.
// you might also need
$ ./automate --discovery
Ready to replace manual work with AI?
Free 1-hour discovery session. We'll map your process and tell you what's worth automating — and what's not.