Newton on Mars — AI-Powered Project Scoping & Consultancy

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RAG Knowledge Systems

Grounded retrieval for massive data stores.

Connecting LLMs to your private data safely is the most valuable step in modern enterprise tech. We construct resilient Retrieval-Augmented Generation (RAG) pipelines that accurately navigate gigabytes of unstructured internal knowledge.

By combining dense vector search, sparse keyword matching, and cross-encoder re-ranking, we achieve near perfect retrieval accuracy, effectively eliminating hallucinations in mission-critical environments.

Key Capabilities

✓Hybrid search architecture (BM25 + Vectors)
✓Automated data ingestion from cloud drives
✓Semantic chunking and embedding logic
✓Real-time sync capabilities
✓Accurate inline citations

TL;DR / Key Takeaways

→Raw vector search is often inadequate for domain-specific acronyms.
→We employ hybrid methodologies to fetch EXACT matches where context matters.
→Results are always paired with transparent source linking.

How It Works

StrategyMap data siloes and define knowledge scope.

BuildCode cleaning scripts, compute dense/sparse vectors.

DeployProvision vector databases (pgvector) and search indices.

OptimizeTune re-ranker top-K relevance over real queries.

Use Cases

Enterprise SearchConnect scattered technical docs seamlessly.

Support AutomationInstant, verified answers drafted for support reps.

Metrics

0.89 Precision@50 Hallucinations

Tech Stack & Tools

pgvectorLlamaIndexCohere Re-rankBM25

Case Study Preview

HEALTHCARE

Giving Clinical Investigators Their Time Back, One Protocol at a Time

A HIPAA-compliant protocol summarisation and Q&A agent — every answer cited, every retrieval logged, deployed inside the client's own VPC.

78%PROTOCOL READ-TIME REDUCTION

HR TECH

How a Talent Marketplace Matched 50,000 Candidates to Live Roles in Real Time

A retrieve-rerank-explain pipeline that turned a keyword-driven talent marketplace into a live, contextual matching engine recruiters and candidates both trust.

3.2xINTERVIEW CONVERSION

CONTENT & BRAND

An In-House Writing Co-Pilot That Sounds Like the Brand, Not the Model

A retrieval-grounded writing co-pilot that scored every draft against brand pillars and cited the assets behind every line — earning a brand team's trust.

6.8xWRITER OUTPUT PER WEEK

— 02

RAG Knowledge Systems

Grounded retrieval for massive data stores.

Key Capabilities

✓Hybrid search architecture (BM25 + Vectors)
✓Automated data ingestion from cloud drives
✓Semantic chunking and embedding logic
✓Real-time sync capabilities
✓Accurate inline citations

TL;DR / Key Takeaways

→Raw vector search is often inadequate for domain-specific acronyms.
→We employ hybrid methodologies to fetch EXACT matches where context matters.
→Results are always paired with transparent source linking.

How It Works

StrategyMap data siloes and define knowledge scope.

BuildCode cleaning scripts, compute dense/sparse vectors.

DeployProvision vector databases (pgvector) and search indices.

OptimizeTune re-ranker top-K relevance over real queries.

Use Cases

Enterprise SearchConnect scattered technical docs seamlessly.

Support AutomationInstant, verified answers drafted for support reps.

Metrics

0.89 Precision@50 Hallucinations

Tech Stack & Tools

pgvectorLlamaIndexCohere Re-rankBM25

Case Study Preview

HEALTHCARE

Giving Clinical Investigators Their Time Back, One Protocol at a Time

A HIPAA-compliant protocol summarisation and Q&A agent — every answer cited, every retrieval logged, deployed inside the client's own VPC.

78%PROTOCOL READ-TIME REDUCTION

HR TECH

How a Talent Marketplace Matched 50,000 Candidates to Live Roles in Real Time

A retrieve-rerank-explain pipeline that turned a keyword-driven talent marketplace into a live, contextual matching engine recruiters and candidates both trust.

3.2xINTERVIEW CONVERSION

CONTENT & BRAND

An In-House Writing Co-Pilot That Sounds Like the Brand, Not the Model

A retrieval-grounded writing co-pilot that scored every draft against brand pillars and cited the assets behind every line — earning a brand team's trust.

6.8xWRITER OUTPUT PER WEEK

What we build.

RAG Knowledge Systems

Key Capabilities

TL;DR / Key Takeaways

How It Works

Use Cases

Metrics

Tech Stack & Tools

Case Study Preview

Giving Clinical Investigators Their Time Back, One Protocol at a Time

How a Talent Marketplace Matched 50,000 Candidates to Live Roles in Real Time

An In-House Writing Co-Pilot That Sounds Like the Brand, Not the Model

What else we build. Tight engagements, production outcomes.

What we build.

RAG Knowledge Systems

Key Capabilities

TL;DR / Key Takeaways

How It Works

Use Cases

Metrics

Tech Stack & Tools

Case Study Preview

Giving Clinical Investigators Their Time Back, One Protocol at a Time

How a Talent Marketplace Matched 50,000 Candidates to Live Roles in Real Time

An In-House Writing Co-Pilot That Sounds Like the Brand, Not the Model

What else we build. Tight engagements, production outcomes.