Turn AI into Revenue

Helping you realize power of AI through your Data

Core Machine Learning Service

Our machine learning expertise helps you extract valuable insights from your data using techniques like Time Series Analysis, Regression, Logistic Regression, Classification, and more. By uncovering hidden patterns and predictive trends, we enable your business to make smarter, data-driven decisions that can increase operational efficiency, improve customer targeting, and ultimately boost profit margins.

Will Customer Renew Your Insurance?

This predictive analytics system determines the likelihood of a customer renewing their vehicle insurance. By focusing marketing and retention efforts on customers with the highest probability of renewal, insurance companies can optimize their resources, save time, and achieve a higher conversion rate. Ultimately, this translates to increased renewals and revenue growth.

Project we have done

ML in Other Industries

Similarly, machine learning enables businesses in Stock Market and Finance industries to make smarter decisions through predictive models, anomaly detection, and automated risk assessment.

For example, in the stock market, ML algorithms analyze historic market data and identify patterns to aid portfolio management and algorithmic trading – leading to increased returns and reduced losses.

In finance, fraud detection systems use ML to spot unusual transaction patterns in real-time, enabling fast prevention of fraudulent activities and reducing financial risk.

Generative AI

Generative AI has been a hot and in-demand technology for the last 3 years, revolutionizing industries globally by creating innovative solutions in text, image, video, and audio generation. These models empower companies to automate creative tasks, generate realistic synthetic content, and deliver personalized experiences at scale.

Newton LLM

Newton LLM is a powerful Retrieval-Augmented Generation (RAG) system developed for educational purposes. It leverages technologies such as Airflow for data ingestion pipelines, PostgreSQL as the primary database, DAG management, and live URLs for dynamic data handling. The RAG pipeline uses Qdrant Vector Database for efficient similarity search, and the model is deployed using FastAPI for high performance.

This system enables educational platforms to provide precise and contextual answers by dynamically retrieving and generating content, greatly improving learner engagement and knowledge retention.

Project we have done

Doctor-AI

Doctor-AI is an advanced AI assistant tailored for the healthcare sector. It integrates a robust data ingestion pipeline, text modeling, and image-to-text conversion capabilities. We leverage Groq Accelerators, RAG pipelines, and Qdrant Vector Database to provide efficient real-time healthcare insights.

Acting as a virtual family doctor, Doctor-AI provides diagnostic suggestions along with warnings to seek second opinions. Additionally, it can analyse medical images including medicines and ECGs, assisting healthcare professionals with critical decision-making.

Project we have done

Alexa: WhatsApp MultiModal Agent

This multi-modal AI agent transforms WhatsApp interaction by combining text, voice, image capabilities, and intelligent context management. Unlike simple chatbots, Alexa has its own memory, works on a schedule, and recognizes location contexts.

It uses SQLite for short-term memory storage and Qdrant for long-term memory. Text models like Groq LLaMA 3.3 90B power its language understanding. Speech-to-text and text-to-speech are handled by Eleven Labs, while image generation uses Nano Banana models. Vision analysis is powered by Groq LLaMA-Vision models.

Project we have done

ML & AI in Various Industries

Beyond these projects, ML and AI are transforming numerous industries. In finance, AI-powered crypto trading agents optimize buy/sell decisions and portfolio management. Autonomous and autopilot technologies in the automotive industry utilize ML for safer and smarter vehicles.

The continuous advancements in AI and generative models open new possibilities across sectors, enabling businesses to innovate, automate, and remain competitive.

Product Building

From zero to launch: rapid discovery, focused MVP, and production-grade releases with observability, security, and a clean handoff playbook.

🔎

Discovery → Scope

Problem framing, user journeys, success metrics, technical feasibility, and ROI mapping to avoid overbuilding.

⚙️

MVP → Iterations

Ship a lovable MVP fast; iterate weekly with analytics, user feedback loops, and measurable adoption lift.

🛡️

Production Ready

CI/CD, monitoring, cost controls, RBAC, PII safeguards, evaluations, tracing, and incident runbooks.

How we build

1️⃣
Define the core use case
Persona, pain, one high-value workflow, and a clear “definition of done”.
2️⃣
Architecture & stack
Next.js, FastAPI, Postgres, Redis, object storage, Docker; for AI: RAG, vector DBs, LangChain, LangGraph.
3️⃣
MVP in weeks
Feature slicing, human-in-the-loop where needed, analytics from day one.
4️⃣
Security & governance
Auth, RBAC, audit trails, PII redaction, environment isolation, cost/latency budgets.
5️⃣
Observability
Tracing, logs, dashboards, golden datasets, offline/online evaluation for AI features.
6️⃣
Scale & handoff
SRE checklists, runbooks, growth experiments, pricing, onboarding, and success playbooks.
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AI SaaS & APIs

RAG, AI Agents, Domain Adapters, Evaluation Harness, Scalable Multi-tenant APIs.

Operations

CI/CD, IaC, alerting, cost guardrails, zero-downtime deploys, blue/green, canaries.

Industry Alert

“By 2026, 95% of AI pilots fail to deliver ROI — not because AI is weak, but because teams lack production‑ready workflow integration and a learning culture.”

Don’t ship demos. Ship reliable systems. Education plus consulting is the fastest path across the gap from prototype to profit.

AI Education & Consulting

Transform teams into operators of production AI. We combine targeted training with hands‑on advisory to embed models into real workflows—securely, observably, and measurably.

🎓

Teaching That Changes Outcomes

  • • Foundations that matter: vector search, tokenization, prompt patterns, evaluation design.
  • • Labs: RAG pipelines, agents vs. workflows, guardrails, PII handling, latency/cost budgets.
  • • Role‑based tracks for PMs, engineers, DS/ML, and leadership—aligned to KPIs and ROI.
  • • Reusable playbooks, golden datasets, and internal “how we ship AI” guides.
🧭

Consulting That Ships to Prod

  • • Discovery → MVP → Production with explicit success metrics and eval harnesses.
  • • Architecture choices: retrieval vs. fine‑tuning, agent vs. deterministic workflow, caching tiers.
  • • Security & governance: RBAC, audit trails, secrets, data retention, redaction, policy guardrails.
  • • Observability: tracing, dashboards, offline/online evals, quality drift & incident runbooks.

Why Most AI Startups Fail in Production

  • • Demo‑driven builds: no golden datasets, no eval gates, no rollback plan.
  • • Brittle prompts/agents: no fallbacks, no semantic cache, unclear SLOs for cost/latency/quality.
  • • Data gaps: stale sources, missing contracts/ownership, low signal‑to‑noise.
  • • Security debt: PII leaks, shadow tools, weak secrets, missing auditability & RBAC.
  • • Zero observability: no tracing, no label‑based error taxonomy, no drift monitoring.
  • • Premature complexity: agents before proven workflows; custom models before PMF.
  • • Ops immaturity: no on‑call, no feature flags/canaries, no incident playbooks.
  • • Misaligned metrics: vanity dashboards over adoption, retention, and P&L impact.
Our De‑Risking Recipe
  • • Define quality KPIs (accuracy, coverage, hallucination rate, TTFT, cost per hundred). Tie to business KPIs.
  • • Build golden datasets + eval harness; add guardrails, fallbacks, semantic caches, circuit breakers.
  • • Ship with tracing, dashboards, alerts; run canaries/shadow traffic before full rollout.
  • • Governance by design: data contracts, retention policies, RBAC, audit trails, policy-as-code.

Want a focused workshop or a production‑readiness audit?

About ExergicLabs

Building dependable AI products—end to end. From Core ML and GenAI to product engineering, education, and production readiness, we prioritize tangible business outcomes over demos.

🎯

Mission

Turn AI from experiments into profit—secure, observable, and reliable in real workflows.

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Principles

  • • Solve one valuable workflow first
  • • Measure quality, cost, and latency
  • • Ship, observe, iterate

Why Us

  • • Production-first architecture
  • • Clear KPIs & evaluation harnesses
  • • Secure, governed deployments

Core ML

Time series, regression, classification, churn & risk models.

GenAI

RAG, agents, multimodal (text, image, audio, video).

Product Building

MVP to production: CI/CD, tracing, guardrails, cost control.

Education & Consulting

Role-based training and production readiness audits.

5+
Projects
100%
Ship Rate (MVPs)
24/7
Support
3w
Typical MVP
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