ThinkEasyLabsThinkEasyLabs
AI solutions across industries

AI solutions built with the right model for the job.

ThinkEasyLabs designs and ships AI products for manufacturing, healthcare, BFSI, retail, and logistics — combining open-source and Claude models so every task gets the model best suited for it, not a one-size-fits-all default.

Models we build with
ClaudeLlamaMistralQwenDeepSeekGemma
Model router
Live routing
Try a query
Summarize this week's flagged claims and draft a response.
Reasoning & drafting → ClaudeRetrieval & embeddings → open-source
14 claims flagged, 3 high-risk. Draft response prioritizes claim #A118 (policy mismatch) — ready for review.
How it fits together

From a plain-English question to a verified answer.

Every query is grounded in your business context and your actual data before it ever reaches a model — then routed to the right mix of open-weight and frontier models for the job.

Scroll to see the full flow →
User Query
A plain-English question, typed or asked.
Business Context
Company glossary, KPIs, and domain rules.
Data Sources
SAP, CSVs, PDFs, and internal databases.
Mix of Open-Weight & Frontier Models
Each step routed to the model built for it.
Output / Dashboards
Answers, charts, and saved dashboards.
Verticals

AI solutions built for how your industry actually works.

Every industry has different data, risk tolerance, and workflows. Click through to see a real example query for each.

Manufacturing & Operations

Production, quality, and procurement copilots that turn plant floor data into decisions.

A plant manager asks a question in plain English instead of building a pivot table. The system pulls production, quality, and purchase data, finds the root cause, and drafts a corrective action.

Example query
Which supplier caused the most scrap in March, and why?
Built with a mix of Claude & open-source models
Our approach

Open-source and Claude, used for what each does best.

We don't bet a client's product on a single model vendor. Drag the slider to see how we think about the trade-off on a real project.

Drag to design a model strategy
Open-sourceClaude
Balanced hybrid
Relative cost53%
Accuracy on hard tasks77%
Best for: Most production systems — open-source handles retrieval and volume, Claude handles reasoning, drafting, and anything customer-facing.
Featured project

An AI analyst for MSME manufacturing teams.

A production case study: a plant-floor data platform we designed, built, and shipped for manufacturing operations teams in India.

Production, purchase, and quality data lived across Excel, PDFs, SAP exports, and databases. By the time reports were consolidated manually, the month was already over — and no one had time to ask a follow-up question.

How it works

From your data to a working pilot, fast.

No lock-in to a single model vendor. We start with the data and constraints you already have.

Discover: We map your data, workflows, and constraints — compliance, latency, budget, and where data has to stay private.
Why ThinkEasyLabs

What changes when you bring us in.

Five things that compound across every team that touches your data — and what each one moves on your metrics.

What we optimise
Without ThinkEasyLabs
With ThinkEasyLabs
Impact on your metrics
TAT for business queries
Days to weeks — every ad-hoc question waits in line for an analyst or IT.
Seconds to minutes — ask in plain English, get an answer with the SQL and chart behind it.
Faster decisions, fewer stalled initiatives.
Access to data
Siloed across Excel, PDFs, SAP exports, and databases — no single source of truth.
One semantic layer over every source, queryable by anyone on the team.
Higher data utilization, fewer duplicate or conflicting reports.
Smart decisions
Calls made on gut feel or a stale monthly report.
Every call backed by live, verified data with a confidence score and an audit trail.
Lower decision risk, faster course-correction.
Costs
Analyst headcount and per-seat BI licensing that doesn't scale with usage.
Open-source models handle routine work, premium models only where they earn it.
Lower cost-per-query, predictable AI spend.
Leakages
Manual, error-prone reporting lets billing errors and risk events slip through.
Automated checks flag anomalies and risk events as they happen.
Fewer revenue leaks, tighter compliance.

Have an AI project in mind? Let's build it.

Tell us about your data and workflows. We'll scope a model strategy — open-source, Claude, or both — and get a pilot in front of your team fast.

Talk to Us
Talk to us about your industry, your data, and what you're trying to build.