AI Engineers Who Ship Production-Grade Intelligence
We build LLM-powered apps, retrieval pipelines, and AI agents that are accurate, observable, and cost-controlled — not just demos.
What are you building?
Tell us what you need — scope and estimate in 24 hours, free.
AI Engineering Services
LLM Applications
Chatbots, copilots, and assistants on GPT, Claude, and open models.
RAG Pipelines
Retrieval-augmented generation over your private data.
AI Agents
Tool-using, multi-step agents with function calling and guardrails.
Fine-Tuning
Domain-adapted models via fine-tuning and instruction tuning.
Vector Search
Semantic search with Pinecone, Qdrant, and pgvector.
ML Ops
Evaluation, monitoring, prompt versioning, and cost tracking.
AI Engineering Capabilities
LLM Orchestration
LangChain, LlamaIndex, and custom orchestration frameworks.
Evaluation
Golden datasets, LLM-as-judge, and regression testing for quality.
Guardrails
Prompt injection defence, PII filtering, and output validation.
Cost Control
Caching, routing, and model selection to cut token spend.
Vector Infra
Chunking, embedding pipelines, and hybrid retrieval.
Python & APIs
FastAPI services, async workers, and streaming responses.
Our AI Engineering Process
Discovery & Eval Set
Define the use case, success metrics, and a golden evaluation set.
Prototype
Build a working prototype, measure accuracy, and iterate on prompts.
Productionise
Add guardrails, caching, observability, and cost controls.
Monitor & Improve
Track quality in production and continuously refine.
- Figma design source files
- Clean documented codebase
- CI/CD pipeline
- SEO and analytics setup
- Performance report
AI Stack We Use
Why Digital Web Weaver for AI?
Accuracy First
We measure quality with real eval sets — not vibes or one-off demos.
Cost-Aware
We routinely cut LLM bills 50–70% with caching and smart routing.
Safe & Secure
Prompt injection defence, PII handling, and output guardrails.
Production-Ready
Observability, versioning, and CI baked in from day one.
We had a promising GPT prototype that fell apart in production — hallucinations, runaway costs, no monitoring. Their AI engineer rebuilt it with a proper RAG pipeline, an eval set, and caching. Accuracy went up and our token bill dropped 65%.
Their engineer built us a customer-support agent that actually resolves tickets instead of deflecting them. Tool calling, guardrails, and a fallback to a human when confidence is low. Deflection rate hit 48% in the first month.
We needed semantic search over 200k internal documents. The AI engineer designed the chunking and embedding pipeline, set up pgvector, and tuned hybrid retrieval. Search relevance is night and day compared to our old keyword system.
Hire AI Engineers Who Ship Real Intelligence
When you hire AI engineers from Digital Web Weaver, you get people who treat large language models as production systems — not weekend demos. We're built for teams that have a promising GPT or Claude prototype (or just a sharp idea) and need it to become accurate, observable, and cost-controlled before it reaches real users. From our base in Vadodara, India, we deliver to companies across India, the UK, South Africa, and Ivory Coast, and our difference is discipline: golden eval sets, guardrails, and token budgets are part of the build, not an afterthought once the bill arrives.
RAG, agents, and evaluation you can trust
Most of what we ship is retrieval-augmented generation over private data, tool-using agents with function calling and human fallback, and semantic search on Pinecone, Qdrant, or pgvector. We measure quality with real evaluation sets and LLM-as-judge testing, so "is the AI actually good?" has a number behind it. Because a lot of that work lives in Python, our AI engineers pair naturally with a dedicated Python developer for the data and ML plumbing, and slot AI into broader business process automation when the goal is to remove human toil rather than add another chatbot.
Built to run, not just to impress
Production is where most AI projects quietly fail, so it's where we start. We wrap models in FastAPI services with streaming responses, caching, and prompt versioning, and expose them through clean API endpoints your existing product can consume — whether that product is an internal tool or a customer-facing SaaS platform. Caching, model routing, and smart selection routinely cut LLM spend 50–70%, and observability is baked in from day one so regressions surface before your users do. Tell us your use case and data, and we'll show you what production AI actually looks like.
AI Engineer FAQ
Ready to hire an AI engineer?
Tell us your use case, data, and goals. We'll match you with 2–3 senior AI engineers within 48 hours — all pre-vetted, immediately available, and ready to ship real AI, not just demos.