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READ ARTICLE →From an idea,
to a scoped,
bookable project.
Newton on Mars is an AI-powered consultancy that turns unclear AI ideas into validated build plans. Our voice and chat agents are grounded in real case studies through RAG; qualify your idea in minutes, then route you to a human.
NOM HIGHLIGHTS
BUILT IN PUBLIC
Most AI consultancies sell decks.
We deliver working products.
Most consultancy websites ask you to fill a form and wait. Newton on Mars gives you a working AI intake analyst on the first page. It uses the same architecture we build for clients: streaming chat, voice input, retrieval over real content, citations, cost controls and human handoff when the project is ready.
Grounded by real work
Every answer can reference published case studies and insights instead of vague claims.
Built for qualified conversations
The agent asks follow-up questions, captures context and routes serious projects to a strategy call.
Designed for trust
The system does not invent hard pricing, timelines, or capabilities. If it does not know, it says so.
HOW WE WORK
Four steps. Minutes, not weeks, to a scoped project.
STEP 01
Land
You hit the homepage. You see what we've shipped. Engage "Talk to AI" or browse the case studies — either way, the product sells itself.
STEP 02
Converse
Chat or voice — your call. Our RAG-grounded agent asks sharp follow-up questions and pulls in real case studies to show you what we've already built in your space.
STEP 03
Qualify
The AI reads the room. High-intent leads get a direct booking link. Medium-intent gets routed to our structured intake. Everyone walks away with something useful.
STEP 04
Meet
Book a 30-minute strategy call. By the time we pick up, we already have your full transcript and an AI-drafted project brief waiting in our queue.
Live · Try it now
Not a chatbot. An intake analyst that never sleeps.
Newton · AI intake analyst
STREAMING
Services
What we build. Tight engagements, production-grade outcomes.
01 / 04
AI Agent Engineering
Streaming chat and voice agents, grounded via RAG. Production-grade pipelines with STT, TTS, cost caps, observability, and a 20-turn safety rail baked in.
02 / 04
RAG Knowledge Systems
Grounded retrieval over your own content. Hybrid dense + BM25, top-k re-ranking, cited answers end-to-end. Webhook-to-RAG under 5 minutes.
03 / 04
Full-stack AI Products
Next.js hybrid frontends (SSG + ISR + SSR), FastAPI backends, deployed on AWS with CI/CD and blue-green rollouts. Lighthouse ≥ 90, WCAG 2.1 AA.
04 / 04
Compliance & Cost Controls
Ship AI you can actually run. GDPR, data retention, reCAPTCHA v3, rate limits, per-session token quotas, global circuit breaker.
Early signals
What founders are saying.
ABOUT NOM
Engineers who build first. Then talk about it.
Newton on Mars is the AI engineering practice of Divam Technologies. We don't sell you on capability — we demonstrate it. No discovery decks, no 40-page scoping documents. You get a real, running prototype on day one, and the code does the convincing.
This website is exhibit A. The chat and voice agents you see above aren't demos — they're the same production-grade infrastructure we build for clients.
"The fastest path from 'I have an idea' to 'I have a scoped, validated, bookable project.'"
We exist for the founder with a half-formed idea and the enterprise CTO who needs proof before a purchase order. We close the gap between ambition and a real, working thing — without the months of back-and-forth that consultancy usually demands.
Most consultancy websites look the same. Ours doesn't — because it works. Newton on Mars is built on three commitments:
- •Show, don't pitch. Every visitor experiences our capability firsthand through AI agents grounded in our actual case studies and past work — not generic responses.
- •Qualify honestly. Our AI intake process is designed to give you structured, useful context about your own project — not to oversell a scope. When we route you to a human, they already know your problem.
- •Trust over speed. We never let AI commit to hard timelines or pricing. Every estimate goes through a human. That's not a limitation — it's the point.
Blogs
Latest Insights.
RAG
Why Vector Search Alone Is Not Optimal RAG Search
Vector search captures meaning, but hybrid retrieval with BM25 adds precision. This guide shows why combining both improves RAG relevance and reduces noisy context.
READ ARTICLE →AI
Don't Build Multi-Agents
Why multi-agent architectures are fragile in 2025, and the two core principles of context engineering that should guide how you build reliable AI agents.
READ ARTICLE →START SOMETHING
Scope your idea in under four minutes.
Answer a few click-first questions, share your details once, then we run up to two AI follow-ups to qualify your project faster.
Response time< 4 business hours
Domainnewtononmars.com
TimezoneGMT+5:30 · Remote friendly
Privacy90-day retention
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