GeekyAnts
Artificial Intelligence Consulting

ML Strategy and Model Discovery

We help you leverage machine learning for your business goals. We deliver pragmatic model development that supports business logic: fine-tuning and optimization where it matters, and lean deployments when cost/latency matters.

Machine learning research often hits a wall when it meets real-world constraints. A model that is accurate in a test environment can still be too slow for your users or too expensive to keep running. We see many projects fail because the engineering side was overlooked during the planning phase.


We help you leverage machine learning and make it a worthwhile investment. We look at the full picture of your data and your infrastructure to find the most efficient path forward. This results in a system that solves real problems without becoming a technical burden. Our partners value this because it turns complex problems into a functional, scalable product.

CUSTOMER STORIES

Client Results and Success

Document Intelligence Platform

99% reduction in manual effort

The solution handles thousands of pages, summarises information, and gives accurate, reliable answers within seconds - dramatically improving speed, consistency, and decision-making.

2 mins

For 1,000–10,000 pages processing

85%+

Accuracy in responses

95%+

Contextual relevance

Architecture Review Assistant

88.33% reduction in TAT for review

The assistant helps tech leads refine their designs so architecture boards receive cleaner, more complete diagrams. This helps teams catch early mistakes, improve quality, and reduce rework.

<1 hr

Tech architecture review time

~70%

Accuracy in identifying issues

95%

Contextual relevance

Automated Reporting & Insights System

99% faster turnaround time for report creation

This system relies entirely on the organization’s own data, ensuring zero bias in all outputs

<1 min

Report turnaround

~85%+

Accuracy in generated reports

~85%

Contextual relevance of insights

Property Inspection Chat Assistant

90% property-data relevance during on-site queries

The assistant delivers accurate responses, while automatically routing sensitive or price-related questions to the admin team to ensure control and compliance.

80%

answer accuracy

~90%

relevance to the property data

2x faster

On-site interactions with voice-enabled queries

Personalized Meal Recommendation System

80% recommendation accuracy

The system analyzes user preferences to provide tailored meal suggestions for every day. This helps the platform drive higher engagement and ensures users find recipes they actually want to cook.

80%

Recommendation accuracy

AI Assisted Clinical Documentation for Dentists

95% accuracy in diagnosis and prescription generation

The platform turns dental conversations into automated medical notes and prescriptions instantly. This allows dentists to approve reports with a single click and stay focused on their patients.

~95%

Accuracy in generated reports

~95%

Contextual relevance

WHAT WE DO

Our Capabilities

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RAG Optimization

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Model Fine-Tuning

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Small Language Model Strategy

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Data Engineering & Curation

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MLOps & Performance Monitoring

What You Get

We Help You Move Beyond Guesswork

Many AI projects start with early assumptions about how a model will perform. We replace those assumptions with technical data and clear benchmarks. Our approach is straightforward: Build for reliability. Align with business logic. Scale with efficiency.

What You Get

  • Full visibility into data pipelines

  • Technical proof for every model decision

  • A shared roadmap for engineering and business teams.

    • WHY TRUST US

    What You Get From a Partnership With GeekyAnts

    These are the concrete outcomes of our engagement:
    A detailed report comparing different models to find the most cost-effective fit for your specific task.
    Clear data showing exactly how much performance improved after fine-tuning or RAG customization.
    A blueprint for where your model lives—whether in the cloud or on the edge—and how we track its health in production.
    A practical guide to keeping your data safe and your models accurate as your requirements change over time.

    FEATURED CONTENT

    Our Latest Thinking in AI/ML

    Discover the latest blogs on Our Latest Thinking in AI/ML, covering trends, strategies, and real-world case studies.
    Build vs Buy: Choosing the Right AI Strategy for Insurance Companies
    Business

    May 15, 2026

    Build vs Buy: Choosing the Right AI Strategy for Insurance Companies

    Build or buy AI for insurance? Learn how to avoid vendor lock-in, lower AI operating costs, and build scalable, compliant insurance platforms.

    Beyond AI Pilots: Building Production-Ready RCM Platforms for Denial Prevention, Coding Accuracy, and Smarter Billing
    Business

    May 15, 2026

    Beyond AI Pilots: Building Production-Ready RCM Platforms for Denial Prevention, Coding Accuracy, and Smarter Billing

    Build production-ready RCM platforms for denial prevention, coding accuracy, smarter billing, compliance, and scalable healthcare AI revenue operations.

    Why AI Insurance Projects Fail in Production
    Business

    May 15, 2026

    Why AI Insurance Projects Fail in Production

    Why do most AI insurance projects fail in production? Discover the hidden architectural, compliance, and scaling gaps behind failed AI deployments.

    A 50-Point Production Readiness Checklist for AI-Generated Products
    Business

    May 14, 2026

    A 50-Point Production Readiness Checklist for AI-Generated Products

    This 50-point AI production readiness checklist helps engineering leaders determine whether an AI-generated prototype is ready for enterprise production, or whether it needs to be hardened, refactored, or rebuilt before launch. It covers five pillars: architecture, model and data readiness, observability, security and compliance, and product and business readiness.

     From MVP to Scale: Designing Architecture for AI-First Products
    Events

    May 11, 2026

     From MVP to Scale: Designing Architecture for AI-First Products

    A panel of architects and engineering leaders at thegeekconf mini 2026 discuss how to build and scale AI-first products — from MVP decisions to production-level challenges. The conversation covers data quality, model selection, security, token economics, and the mindset teams need to navigate a fast-moving AI landscape.

    The AI native Enterprise Evolution | Saurabh Sahu
    Events

    May 7, 2026

    The AI native Enterprise Evolution | Saurabh Sahu

    Explore Saurabh Sahu’s insights on AI-native enterprise, AI gateways, model governance, agentic SDLC, and workspace.build for scalable AI adoption from thegeekconf mini 2026.

    View all blogs

    Ready to Turn ML Investment to ROI?

    Schedule a technical discovery session with our machine learning team today.

    Trusted By

    Book a Discovery Call

    Ready to Turn ML Investment to ROI?

    Schedule a technical discovery session with our machine learning team today.

    Trusted By

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