THE ALGO LABS EXPERIENCELEARNING, ENGINEERED.

01 / ENTER THE LAB

Don’t just
learn AI.
Build what’s
next.

Live programmes in AI engineering, business analytics and data science. Learn through guided practice and projects you can explain and demonstrate.

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LIVE GUIDANCE·REAL PROJECTS·APPLIED AI

ONE FOUNDATION.
FOUR DIRECTIONS.

01 GenAI & Agentic AI 02 FDE 03 Business Analytics 04 Data Science

SCROLL TO DISCOVERYour journey from idea to impact EXPLORE THE NEW PATHWAYForward Deployed AI Engineering

Career support is available for eligible learners. No job or placement guarantee is provided.

02 / Programme pathways

Choose your direction.

Different careers require different combinations of technology, analytics, engineering and business capability. Select a pathway to open its detail.

GenAI & Agentic AI

What is it: Applied LLM engineering: prompt design, retrieval systems and autonomous agent workflows.
Who is it for: Developers and technical professionals moving into AI engineering.
What you build: Enterprise RAG assistant and a multi-agent workflow you can deploy.
Duration: 13-Week Intensive / 4-Month Weekend

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Forward Deployed AI Engineering

What is it: End-to-end enterprise AI delivery: discover, scope, architect, build, deploy, adopt.
Who is it for: Engineers and consultants who want to own delivery, not just models.
What you build: A deployed enterprise AI solution integrated with real systems.
Duration: 6 Months Training
Lifecycle: Discover→Scope→Architect→Build→Deploy→Adopt

Explore Programme

Business Analytics + GenAI

What is it: Business analysis, SQL, BI dashboards and GenAI-assisted analytical workflows.
Who is it for: Graduates, analysts and business professionals from any background.
What you build: Decision dashboards plus a GenAI reporting and automation workflow.
Duration: 6 Months Training + 3 Months Internship

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Data Science + GenAI

What is it: Python, statistics and machine learning progressing into NLP, deep learning and GenAI.
Who is it for: STEM graduates and career switchers starting from fundamentals.
What you build: A validated ML model and a grounded AI application in production.
Duration: 6 Months Training + 3 Months Internship

Explore Programme

Compare all four pathways

Programme comparison

Four pathways, side by side.

Compare the four pathways to find the one that matches your background and career goal.

Compare focus, coding level, delivery emphasis and best fit

Comparison	GenAI & Agentic AI	Business Analytics	Data Science	FDE
Primary Focus	AI application engineering	Business decisions & reporting	Models & predictive systems	Enterprise AI delivery
Coding Intensity	Intermediate	Foundation	Intermediate	Advanced
Business Interaction	Medium	High	Low to medium	Critical
Deployment Emphasis	High	Moderate	Moderate	Very high
Best Fit	Developers building AI products	Analysts & business professionals	Graduates entering data careers	Engineers owning end-to-end delivery

GenAI & Agentic AI

Primary Focus: AI application engineering
Coding Intensity: Intermediate
Business Interaction: Medium
Deployment Emphasis: High
Best Fit: Developers building AI products

Business Analytics

Primary Focus: Business decisions & reporting
Coding Intensity: Foundation
Business Interaction: High
Deployment Emphasis: Moderate
Best Fit: Analysts & business professionals

Data Science

Primary Focus: Models & predictive systems
Coding Intensity: Intermediate
Business Interaction: Low to medium
Deployment Emphasis: Moderate
Best Fit: Graduates entering data careers

FDE

Primary Focus: Enterprise AI delivery
Coding Intensity: Advanced
Business Interaction: Critical
Deployment Emphasis: Very high
Best Fit: Engineers owning end-to-end delivery

03 / FROM QUESTION TO SYSTEM

Follow one idea.
See what it takes to build it.

See the skills behind a policy assistant: framing a task, connecting evidence, testing an answer and planning delivery. This GenAI and FDE example is an illustrative walkthrough with authored sample outputs, not a live AI application.

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01 / Frame the request

A question becomes a clear task.

An employee asks how to request access. Start by defining what the system may answer and which actions need approval.

Skill in focusProblem framing and scope.

request.json

Question: How do I request access?

Scope: explain the approved process

Boundary: never grant access automatically

02 / Connect the evidence

Give the answer somewhere to come from.

Retrieve the relevant policy passage and keep its source attached. A plausible answer is not enough without evidence.

Skill in focusRetrieval and source attribution.

retrieval.trace

Source: sample access policy / section 2

Passage: submit a request to the owner

Evidence: passage identifier retained

03 / Build the response

Turn the passage into a useful answer.

Compose a short response with a citation, then prepare a ticket draft. A person stays in control of any action.

Skill in focusWorkflow design and approval boundaries.

response.preview

Answer: Send an access request to the owner.

Citation: sample policy / section 2

Action: ticket draft awaiting your review

04 / Test the boundary

The system must know when to stop.

Try a request the policy does not cover. The expected behaviour is to flag missing evidence and ask for review.

Skill in focusEvaluation and failure handling.

evaluation.case

Test: request an undocumented permission

Expected: insufficient evidence

Decision: human review, no automatic grant

05 / Prepare for delivery

Make the whole journey inspectable.

Package the application with a request trace, health checks and an operator guide. Delivery includes knowing how to investigate and recover.

Skill in focusTraceability and delivery planning.

release.checklist

Trace: request → source → answer → review

Before release: health check and rollback plan

Handover: operator walkthrough and ownership

This is the work behind an AI system: connecting evidence, making decisions and checking what happens next.

Find your programme ↑

CONNECT THE PROCESS TO YOUR PATHWAY

Different disciplines. Different things to build.

Analytics and Data Science apply evidence and evaluation to different outputs. These illustrative examples show each pathway's focus; explore the curriculum for its full scope.

GenAI & Agentic AI

A source-backed policy assistant

The walkthrough above illustrates retrieval, response generation and a controlled agent workflow.

Explore curriculum

Business Analytics & Generative AI

A dashboard explaining a sales change

Start with a business question, query the data with SQL and use a BI dashboard to explain the evidence behind a decision.

Explore curriculum

Data Science & Generative AI

A forecast tested against held-out data

Frame a prediction task, prepare data in Python and evaluate a model before interpreting its results and limitations.

Explore curriculum

Forward Deployed AI Engineering

An AI workflow prepared for operational use

Extend the assistant story into discovery, integration, deployment and adoption, with ownership of the handover.

Explore curriculum

Explore the full learning journey

From first principles.
To real possibility.

0106

Learn

Start with the why.

Understand the foundations of AI, data, and analytics. Build intuition before adding complexity.

YOU WALK AWAY WITH

Working notebooks and fundamentals you can explain and defend.

See what you’ll build

YOUR CAPABILITY STACK

Learnfoundations.ipynb01

Buildretrieval_pipeline.py02

Integrateconnected_workflow03

Evaluateevaluation_report04

Deployapplication.release05

Demonstratecapstone.walkthrough06

Illustrative artifactfoundations.ipynb

Inspect your first dataset

Input → support_requests.csv
Check → missing values and column types
Output → a documented data-quality notebook

Understand → experiment → explain1 / 6

01 / Learn
Start with the why.
Understand the foundations of AI, data, and analytics. Build intuition before adding complexity.
YOU WALK AWAY WITH
Working notebooks and fundamentals you can explain and defend.
Inspect a sample artifact
Illustrative artifactfoundations.ipynb
Inspect your first dataset
- Input → support_requests.csv
- Check → missing values and column types
- Output → a documented data-quality notebook
02 / Build
Make the first thing work.
Turn concepts into working components. Write the code, connect a model, and test it on your own data.
YOU WALK AWAY WITH
A retrieval or model component running on your own data.
Inspect a sample artifact
Illustrative artifactretrieval_pipeline.py
Retrieve a source passage
- Question → How do I request access?
- Source → sample_access_policy.md · §2
- Output → a passage with its source identifier
03 / Integrate
Connect the possibilities.
Bring models, data, APIs, and tools together. Move from an isolated experiment to an end-to-end workflow.
YOU WALK AWAY WITH
An end-to-end workflow wired across APIs and tools.
Inspect a sample artifact
Illustrative artifactconnected_workflow
Trace one request across services
- Request → access enquiry
- Route → API → retrieval → response
- Output → one trace ID across the workflow
04 / Evaluate
Make quality intentional.
Test what happens beyond the happy path. Measure quality, reliability, security, and performance.
YOU WALK AWAY WITH
An evaluation set with recorded quality and reliability results.
Inspect a sample artifact
Illustrative artifactevaluation_report
Record a failure worth fixing
- Case → a question outside the source material
- Expected → say evidence is insufficient
- Review → inspect the answer and cited passage
05 / Deploy
Take it into the world.
Move beyond your own screen. Package and host a working application that other people can actually use.
YOU WALK AWAY WITH
A hosted application other people can actually use.
Inspect a sample artifact
Illustrative artifactapplication.release
Prepare a release checklist
- Package → application and dependencies
- Verify → configuration and health endpoint
- Recover → document the rollback procedure
06 / Demonstrate
Show what you can do.
Tell the story behind your system. Explain the decisions, demonstrate the outcome, and make your capability visible.
YOU WALK AWAY WITH
A capstone walkthrough with technical and business evidence.
Inspect a sample artifact
Illustrative artifactcapstone.walkthrough
Explain the finished system
- Show → input, workflow and output
- Explain → a trade-off and a failure case
- Evidence → demo, repository and evaluation notes

Explore student projects

Explore more projects and the FDE delivery process

04 / Project showcase

Build something you can explain.

Scroll to bring a system to life. Explore the decisions and engineering behind each project.

Forward Deployed EngineeringFeatured FDE project

Enterprise Workflow Deployment

Operations teams manually transfer information between documents, databases and business applications.

SCROLL TO ASSEMBLE100%

SynchroniseInspect the business-system connection and synthetic audit trail.

invoice → validation → approval → synchronisationIllustrative preview

Extracted data

SupplierNorthstar Supplies

InvoiceINV-1042

Amount₹48,500

Purchase orderPO-2081matched

Sample data — synthetic invoice.

Workflow

1. Incoming document
invoice_1042.pdf received
2. Extracted data
Supplier, invoice, amount, PO reference
3. Validation
Fields present · PO matched · no duplicate
4. Human review
Awaiting finance approval
5. Database
Approved record saved
6. Business system
ERP sync complete

Audit trail

10:02 document received · invoice_1042.pdf

10:02 fields extracted · 4 of 4

10:03 validation passed · duplicate check clear

10:11 approved by finance reviewer

10:11 record saved · ERP sync complete (1 attempt)

Inspect an example output Illustrative · separate from the demo state

Sample record · not a live result

Invoice review record

Document: sample_invoice.pdf
Validation: Supplier reference missing
Next action: Hold synchronisation and request a correction

What to reviewCheck required fields and duplicate detection. Only approved, validated records should proceed to synchronisation.

Explore the workflow

Illustrative preview — sample data, not a live service.

What you build: An integrated workflow that processes an incoming invoice, validates its fields, requests approval and synchronises approved data.
Technology: FastAPI, PostgreSQL, REST APIs, OAuth, Docker, Azure
Deliverable: An enterprise workflow with validation, human approval, reliable synchronisation and audit logs.

View all projects

Enterprise RAG Assistant

Employees struggle to find reliable answers across scattered company documents.

An assistant that searches internal documents and produces answers supported by source citations.

Python, LangChain, vector database, hybrid search, FastAPI

A knowledge assistant with traceable answers and retrieval evaluation.

Document ingestion, chunking and metadata design.
Hybrid retrieval combining keyword and semantic search, with reranking.
Citation validation so every claim maps back to a passage.
Handling questions without sufficient evidence instead of guessing.

Agentic Workflow System

Service requests require repetitive classification, information gathering and coordination across tools.

An agent workflow that interprets a request, looks up relevant information, prepares an action and requests human approval.

Python, LangGraph, REST APIs, PostgreSQL, FastAPI

A multi-step agent with controlled tool execution and a visible activity history.

Agent state modelling and tool schemas with validation.
Approval gates separating proposed actions from executed actions.
Retries and timeouts around every external call.
Execution traces so a run can be inspected end to end.

Enterprise Workflow Deployment

Operations teams manually transfer information between documents, databases and business applications.

An integrated workflow that processes an incoming invoice, validates its fields, requests approval and synchronises approved data.

FastAPI, PostgreSQL, REST APIs, OAuth, Docker, Azure

An enterprise workflow with validation, human approval, reliable synchronisation and audit logs.

Workflow discovery with the team that does the work today.
API integration, field mapping and authentication.
Duplicate prevention and retry handling on synchronisation.
Auditability and deployment into the customer environment.

AI Operations Command Center

Teams need visibility into AI application failures, performance and operating costs after deployment.

A monitoring workspace that connects service health, execution traces, evaluation results and incident response.

OpenTelemetry, Application Insights, FastAPI, PostgreSQL, Docker

An operations dashboard with alerts, diagnostic traces and a recovery runbook.

Health checks and distributed tracing across services.
Latency, cost and quality evaluation tracking.
Alert thresholds and fallback handling.
Incident diagnosis from trace to root cause.

Client Deployment & Adoption

A customer rollout requires acceptance testing, user onboarding and evidence that people can use the solution successfully.

A deployment workspace for tracking pilot readiness, UAT, rollout, training and adoption.

Azure, GitHub Actions, PostgreSQL, role-based access, product analytics

A customer handover package with UAT evidence, rollout controls, onboarding materials and adoption reporting.

Success criteria and pilot planning agreed with the customer.
UAT execution, defect tracking and release gates.
Onboarding material and usage measurement.
Hypercare and structured handover.

Enterprise Claims Transformation

Claims teams review documents manually and coordinate decisions across fragmented systems.

An end-to-end customer solution covering discovery, document processing, policy retrieval, business-rule validation, human review, integration and deployment.

Python, FastAPI, PostgreSQL, RAG, LangGraph, Docker, Azure

A complete FDE capstone with discovery artefacts, application, evaluation, deployment plan, UAT and business-impact measurement.

Stakeholder discovery and process mapping.
Architecture and sensitive-data handling.
Integration, evaluations and human oversight.
Deployment and adoption planning.

05 / Forward Deployed AI Engineering · New

Building the AI is only half the job. Own the outcome.

Illustrative internal support-assistant project

01 / Discover

Understand the business problem before proposing any system.

In the illustrative internal support-assistant project, support agents answer repeated questions by searching scattered policy documents and past tickets. Discovery establishes who is affected, which requests recur, and where the current process actually breaks.

Interview support agents and their team lead
Sample recurring request types
Locate the systems documents already live in
Agree what a useful answer looks like

AL / DELIVERY SYSTEM01 / 06

1 of 6 delivery stages assembled

Problem understood

Support problem: Where the process breaks
Stakeholder needs: Agents, team lead, compliance
Recurring requests: Sampled request types

Illustrative delivery map: each stage adds its own artifacts and earlier ones stay in place. No results or client work are implied.

01 / Discover
Understand the business problem before proposing any system.
In the illustrative internal support-assistant project, support agents answer repeated questions by searching scattered policy documents and past tickets. Discovery establishes who is affected, which requests recur, and where the current process actually breaks.
- Interview support agents and their team lead
- Sample recurring request types
- Locate the systems documents already live in
- Agree what a useful answer looks like
Problem understood
Support problem
Where the process breaks
Stakeholder needs
Agents, team lead, compliance
Recurring requests
Sampled request types
02 / Scope
Turn the problem into a delivery boundary with success criteria.
Scope fixes what the assistant will and will not do: retrieve and summarise approved internal documents for agents, with sources shown. Out of scope: replying to customers directly, changing records, and anything requiring data the team cannot share.
- Define in-scope request types
- Write acceptance criteria with the team
- Record data access and privacy constraints
- Set review and escalation expectations
Delivery boundary agreed
Pilot boundaries
In scope / out of scope
Acceptance criteria
Agreed with the team
03 / Architect
Design the retrieval, application and integration layers.
The architecture is a retrieval-grounded assistant: documents are ingested and chunked, embedded into a vector store, and retrieved at question time so the language model answers from approved internal content rather than memory.
- Ingestion and chunking strategy
- Vector store and retrieval design
- Prompt and grounding contract
- Authentication and access boundaries
Design set
Data sources
Approved internal content
Access permissions
Who may see what
Integration design
Systems and boundaries
04 / Build
Implement the pipeline, application and evaluation harness.
Building means the working system: an ingestion pipeline, a retrieval service, an answer endpoint that cites its sources, and an agent-facing interface. An evaluation set of representative questions is written alongside the code, not after it.
- Ingestion + embedding pipeline
- Retrieval and answer service
- Source citations in every response
- Evaluation set of representative questions
Working system
Retrieval components
Ingest, index, retrieve
Application
Answer service with citations
Workflow components
Agent-facing flow
Retrieval pipeline (within Build)
1. Documents
2. Ingest + embed
3. Index
4. Retrieve
5. Grounded answer
05 / Deploy
Run it where the work happens, with monitoring and guardrails.
Deployment integrates the assistant into the tools agents already use, behind existing sign-in. Logging, latency and failure handling are part of the delivery, along with a defined path for reporting a wrong or unhelpful answer.
- Integrate with the existing support tool
- Sign-in and permission checks
- Logging, latency and error monitoring
- Fallback when retrieval finds nothing
Running in the workflow
Release process
Staged rollout
Monitoring
Logging, latency, errors
Handover
Runbook and owners
06 / Adopt
Own the outcome: adoption, feedback and iteration.
Adoption is where delivery is judged. The team is onboarded, feedback is collected in the flow of work, gaps in the document set are fixed, and the assistant is iterated with its users rather than handed over and left.
- Onboard the support team
- Collect in-product feedback
- Close gaps in the document set
- Iterate on retrieval and prompts
Outcome owned
Onboarding
Support team enabled
Feedback
Collected in the flow of work
Measurement plan
What the team will track

6 Months TrainingDuration

120 HoursLive learning

Explore the FDE diploma Read the career guide

Live instructor-led learning
Production-oriented projects
RAG + AI agents
Deployment experience
Supervised project pathways

Teaching approach

How the learning actually works.

Live guidance, structured practice and project work connect each programme's curriculum to something you can build and explain.

Goal: Connect the skills you learn to a defined problem and a practical output.
Projects: Build dashboards, models or AI applications according to your pathway.
Depth: Understand the data, methods and decisions behind your work.
Evidence: A reviewable project portfolio you can walk someone through.
Practice: Use guided exercises and project work to test what you have learned.

Who teaches

Rohit Krishnan M

Lead Trainer & AI Architect

Lead trainer and AI architect for Algo Labs programmes. Focused on building industrial-grade GenAI and Agentic AI systems.

Trainer profile

Career pathways, credentials and delivery options

Explore roles, assessment conditions, credential eligibility and learning locations. Career support does not guarantee a job or placement.

Career roles

Where these skills are used.

Roles that align with skills developed across Algo Labs engineering pathways.

AI EngineerCore
Forward Deployed AI EngineerFDE
GenAI ArchitectGenAI
AI Agent DeveloperGenAI
Data ScientistDS
Business Analytics SpecialistBA

Role alignment depends on programme selected, prior experience, project capability and employer requirements. Career outcomes are not employment guarantees.

Credentials

Evidence of what you completed.

Programme credentials are tied to applicable completion and assessment requirements. Selected pathways may also include preparation for external Microsoft/IBM examinations.

Explore credentials

Algo Labs credentials

Issued upon successful completion of all assessed modules, project submissions, and technical evaluation.

Professional Diploma in AI Engineering
Specialist Certification in Generative AI & Agents
Internship Completion Evidence (Eligible Tracks)

Optional external preparation

We provide curriculum mapping and preparation support for major industrial cloud and AI certifications.

Microsoft and IBM credentials, where referenced, are optional external certification pathways governed by the respective providers. Algo Labs programme credentials are distinct from these third-party certifications. Algo Labs does not issue Microsoft or IBM certifications directly.

Microsoft Preparation Support: Azure AI Fundamentals (Exam-based)
Microsoft Preparation Support: Azure AI Engineer Associate (Exam-based)
IBM Preparation Support: Applied AI Professional

Learner support

Algo Labs is a live online institute headquartered in Trivandrum, with local support hubs in major tech cities.

All support hubs

Questions

Before you choose a pathway.

What is Algo Labs?

Algo Labs is an applied AI training institute based in Thiruvananthapuram, Kerala. Its four primary pathways cover GenAI and agentic AI, business analytics, data science, and Forward Deployed AI Engineering.

About Algo Labs and its official contacts

Which Algo Labs programme should I choose?

Choose GenAI & Agentic AI for AI applications and agent workflows; Business Analytics for SQL, dashboards and business decisions; Data Science for statistics, machine learning and predictive models; or Forward Deployed AI Engineering for enterprise delivery from discovery to adoption. Check each curriculum and its prerequisites before choosing.

Compare the four programme curricula

Do I need coding experience?

Not for every pathway. Business Analytics and Data Science begin with foundational Python and SQL modules. GenAI & Agentic AI and Forward Deployed AI Engineering assume you are comfortable writing code.

Are classes live?

Yes. All primary programmes are delivered as live instructor-led sessions, so you can ask questions, debug with the trainer and get feedback in real time.

Are weekday and weekend options available?

Yes. Programmes run in weekday and weekend formats depending on the batch. Speak with the admissions team for the schedules currently open.

Will I work on projects?

Yes. Project work depends on your pathway: business analysis and dashboards, predictive models, retrieval assistants, or agent workflows. Programme pages explain the project and assessment requirements. The Projects gallery shows illustrative briefs, not verified student outcomes.

Explore illustrative project briefs

What credentials are available?

Credentials and completion requirements depend on the programme. External certification preparation is separate from certification awarded by that provider; check exam, assessment and fee requirements with admissions.

Does Algo Labs guarantee a job or placement?

No job or placement guarantee is provided. Career support depends on the applicable programme and completion requirements. Ask admissions about eligibility and the support included before enrolling.

Ask about career support and eligibility

How can I confirm fees and the next batch?

Contact admissions for the current fee, payment terms, batch dates and delivery arrangements for your chosen programme. Ask which assessments, credentials and internship conditions apply before making a decision.

Enquire about programme fees and batches

How do I speak with the admissions team?

Use the contact form to prepare an enquiry, then send the draft through your email app or WhatsApp. You can also call the admissions number in the footer. Preparing a draft does not send a message or confirm a booking.

Book Free Counselling

Insights & career guides

Deep-dive resources for AI engineering and adoption.

View all resources

06 / Admissions

Your next chapter starts here.

Tell us your goals and experience. The Algo Labs team can help you choose a learning pathway.

Book Free Counselling Explore programmes

Opens a short enquiry form so you can request counselling.

Chat on WhatsApp Call Admissions+91 81474 49088

Don’t justlearn AI.Build what’s next.

Choose your direction.

GenAI & Agentic AI

Forward Deployed AI Engineering

Business Analytics + GenAI

Data Science + GenAI

Four pathways, side by side.

GenAI & Agentic AI

Business Analytics

Data Science

FDE

A question becomes a clear task.

Give the answer somewhere to come from.

Turn the passage into a useful answer.

The system must know when to stop.

Make the whole journey inspectable.

Different disciplines. Different things to build.

GenAI & Agentic AI

Business Analytics & Generative AI

Data Science & Generative AI

Forward Deployed AI Engineering

From first principles.To real possibility.

Start with the why.

Start with the why.

Make the first thing work.

Connect the possibilities.

Make quality intentional.

Take it into the world.

Show what you can do.

Build something you can explain.

Enterprise Workflow Deployment

Invoice review record

Understand the business problem before proposing any system.

Turn the problem into a delivery boundary with success criteria.

Design the retrieval, application and integration layers.

Implement the pipeline, application and evaluation harness.

Run it where the work happens, with monitoring and guardrails.

Own the outcome: adoption, feedback and iteration.

Understand the business problem before proposing any system.

Turn the problem into a delivery boundary with success criteria.

Design the retrieval, application and integration layers.

Implement the pipeline, application and evaluation harness.

Run it where the work happens, with monitoring and guardrails.

Own the outcome: adoption, feedback and iteration.

How the learning actually works.

Rohit Krishnan M

Where these skills are used.

Evidence of what you completed.

Algo Labs credentials

Optional external preparation

Before you choose a pathway.

What Is Agentic AI?

RAG Explained

Become an AI Engineer

Your next chapter starts here.

Don’t just
learn AI.
Build what’s
next.

From first principles.
To real possibility.