AI SOLUTIONSAI IMPLEMENTATIONAUTOMATIONAI PRODUCT

I turn business problems into AI products, automated workflows, and scalable solutions.

I work across AI implementation, workflow automation, agentic systems, and AI product delivery — translating business problems into practical solutions using LLMs, APIs, automation platforms, RAG, structured data, and human-in-the-loop controls. My experience spans hands-on solution building, 0→1 product development, business operations, customer workflows, and GTM execution.

View My Work Download Résumé

M.S. Business Analytics · AI Implementation · 0→1 Product Development · Founder Experience

Architecture

Problem / Intelligence / Orchestration / Human control / Outcome

Operating architectureFig. 01

01Input

Input

Business problem

Bottleneck, manual load, cost

Workflow context

Systems of record, documents

02Understand

Understand

Intent & context resolution

Classify, extract, retrieve, disambiguate

03Orchestrate

Orchestrate

Orchestration layer

LLM reasoning · deterministic rules · routing · retries

04Tools & data

Tools & data

Models

Tools & APIs

Data & records

05Control

Control

Decision gate

Confidence, risk and policy decide what proceeds

06Action

Action

Execute

Automated action in the workflow

Human review

Approval, correction, accountability

07Outcome

Outcome

Operations outcome

Growth outcome

Evidence

$50K/mo

Peak agency revenue built and operated

4h → 25m

Screening time per evaluation cycle at ServIT

58% → 81%

First-assignment routing accuracy with TriageKit at Dextara

Solution lanes

Where I create value

Two sides of one problem: implementing AI that works, and shipping AI products that get used.

Lane 018 relevant roles

AI Solutions & Implementation

AI implementation, business automation, agentic workflows, RAG systems, integrations, human-in-the-loop controls, and solution architecture focused on measurable operational outcomes.

AI Implementation
AI Solutions
AI Automation
AI Deployment
AI Solutions Consulting
AI Enablement
AI Transformation
Solutions Architecture

Explore AI Solutions

Finance operationsPreview A

01Input

Input

Invoices & documents

Ledger records

02Understand

Understand

Extraction & reconciliation

03Orchestrate

Orchestrate

Exception routing & checks

04Gate

Gate

Variance threshold

05Human

Human

Finance review

06Outcome

Outcome

Clean close, fewer exceptions

Lane 026 relevant roles

AI Product & Delivery

0→1 AI product development, product discovery, roadmap prioritization, requirements definition, MVP scoping, AI evaluation, agile delivery, and cross-functional execution.

AI Product Manager
Product Operations
Technical Product Manager
AI Product Strategy
AI Product Delivery
AI Platform / Automation Product roles

Explore AI Product Work

Product deliveryPreview B

01Input

Input

Problem & requirements

Discovery · user and business needs

02Scope

Scope

MVP definition

Roadmap prioritization · scoping

03Build

Build

Solution build

Agentic workflows · RAG · integrations

04Evaluate

Evaluate

AI evaluation

Accuracy · edge cases · cost

05Ship

Ship

Release & adoption

Cross-functional delivery · rollout

06Outcome

Outcome

Working product

Adopted · iterated with real usage

Selected work

Systems I’ve designed

Examples of how I move from business problem to implemented AI workflow — each one an architecture with orchestration, control and a measurable outcome.

01AI Business Solutions

Conversational Analytics Agent

SYSTEM / CONVERSATIONAL ANALYTICS

A business analytics system combining deterministic data workflows, conversational querying, human-review controls, and scheduled operational monitoring across five connected business tables.

Conversational analytics workflowFig. 02

01Input

Input

Business Data

5 operational tables · 3,680 records

02Clean

Clean

Validation & Cleaning

Deterministic rules · auto-corrections

03Orchestrate

Orchestrate

AI Intent Layer

Intent resolution · parameter extraction

04Tools

Tools

Deterministic Query Workflows

n8n calculations · joins · aggregations

05Control

Control

Human Review & Monitoring

Exception queue · threshold anomaly detection

06Outcome

Outcome

Business Insight

Verified answers · executive reporting

Proof

74 / 79
Seeded data issues detected (94% detection rate)
30% → 60%
Query accuracy across evaluation rounds
5.05s
Median query latency

Conversational Analytics
Data Quality
Deterministic Workflows
Human-in-the-Loop
Monitoring

View Case Study

02AI Business Solutions

AI-Augmented Finance Operations & Control System

SYSTEM / FINANCE OPERATIONS

A finance operations system combining deterministic accounting controls with narrowly scoped AI for document extraction and grounded communication — covering billing, AR collections, expense categorization, reconciliation, forecasting, liquidity monitoring, and live-source verification.

Deterministic logic owns financial truth. AI operates inside validated boundaries.

Finance control & verification workflowFig. 03

01Input

Input

Finance Data

02Controls

Controls

Deterministic Controls

03Package

Package

Verified Fact Package

04AI Layer

AI Layer

AI-Assisted Generation

05Validate

Validate

Deterministic Validation

06Gate

Gate

PASS / BLOCK

07Outcome

Outcome

Verified Artifact

Proof

$10.4M
Balanced ledger
578
Broken account relationships reconstructed
$6.815M
Outstanding AR cross-verified
11 / 11
Headline figures re-verified from live data

Finance Operations
Deterministic Controls
AI Guardrails
Reconciliation
Source Verification

View Case Study

03Agentic AI

Company OS — Multi-Agent Business Workflow Orchestrator

A supervisor/router architecture coordinating five specialized domain agents, with human approval required before high-risk financial actions, customer communication, or system-of-record updates.

Built as an internal system, not a production-scale deployment.

Agent orchestrationFig. 05

01Input

Input

Task request

Business context

02Orchestrate

Orchestrate

Supervisor · router

03Agents

Agents

Agent A

Agent B

Agent C

Agent D

Agent E

04Gate

Gate

Approval gate · guardrails

05Human

Human

Owner approves high-impact action

06Outcome

Outcome

Executed action

Cost tracked

Architecture

Safeguards for API outages · Malformed inputs · Rate limits · Credential expiration · Model timeouts

Additional systems

04AI Business Solutions

AI Recruiting Automation

Recruiters spent most of the week reading applications. The workflow parses applications against role requirements, scores and explains each match, and sends only borderline or shortlisted candidates to a review queue. The hiring decision stays with the recruiter.

Recruiting workflowFig. 04

01Input

Input

Inbound applications

Job requirements

02Understand

Understand

Parse, structure & match scoring

03Orchestrate

Orchestrate

Shortlist reasoning & ranking

04Gate

Gate

Confidence threshold

05Human

Human

Recruiter review queue

06Outcome

Outcome

Final hiring decision

Outcome

4 hours → 25 minutes screening time

Recruiting Operations
Workflow Automation
Human-in-the-Loop

05Commercial & GTM

AI Lead Response & Qualification

Inbound leads went cold before anyone replied. The system reads intent, qualifies against fit criteria, replies and schedules automatically, keeps CRM stages in sync, and separates sales-ready leads from those a person should nurture.

Built from the operating reality of running a performance marketing agency.

Lead response workflowFig. 06

01Input

Input

Inbound lead

Campaign source

02Understand

Understand

Qualification & intent scoring

03Orchestrate

Orchestrate

Personalized follow-up sequencing

04Tools

Tools

SMS / email

Calendar

CRM

05Gate

Gate

Qualified vs nurture

06Human

Human

Sales owns the conversation

07Outcome

Outcome

Appointment booked

CRM stage synced

Architecture

Lead Operations
CRM Automation
Appointment Scheduling

06Commercial & GTM

Content Performance Engine

Less a writing tool than a governed workflow: research and brand constraints frame each draft, claims are fact-checked, a person approves before anything ships, and GA4 and Meta results feed back into what gets made next.

Content workflowFig. 07

01Input

Input

Research

Brand constraints

02Orchestrate

Orchestrate

Brand-constrained drafting

03Validate

Validate

Fact-checking & source trace

04Gate

Gate

Publish-ready check

05Human

Human

Human approval

06Outcome

Outcome

Published asset

GA4 / Meta feedback loop

Architecture

Research → Drafting → Validation → Human approval → Performance feedback

Content Operations
Marketing Analytics
Governed Workflow

Experience

Commercial & GTM experience

Built and operated customer-acquisition, CRM automation, performance marketing, and lifecycle workflows through ClientCraft, combining AI-assisted operations with measurable revenue and growth outcomes.

01Input

Research

Audience and market research

02Orchestrate

Campaign

Copy, creative ideation, landing pages

03Input

Lead Capture

Inbound workflows

04Understand

Qualification

AI-assisted scoring and classification

05Orchestrate

Follow-Up

Personalized SMS/email sequences

06Tools & data

CRM

Stage synchronization and routing

07Tools & data

Analytics

GA4, Meta and performance reporting

08Feedback

Iteration

Identify underperforming assets and recommend improvements

Marketing performance is a system, not a set of tools — which is exactly where orchestration, control and measurement earn their place.

Operating model

How I implement

Start with the workflow, not the model. The same five-stage operating model runs whether the outcome is an internal operations workflow or a marketing growth system.

Diagnose

Understand the business problem, bottleneck and current operating cost.

Produces — Problem statement

Map

Identify decisions, handoffs, data sources, systems and failure points.

Produces — Workflow map

Design

Decide what AI should handle, what automation should handle and where humans should remain involved.

Produces — Architecture

Implement

Connect models, workflows, APIs, databases, CRM systems and business tools.

Produces — Working system

Measure

Evaluate reliability, adoption, operating cost, time saved and business impact.

Produces — Evidence

Credibility

Why this work is credible

My AI work is grounded in real operating environments — not just tools, prompts or demo workflows.

Product Foundation

Built products from discovery to adoption

Dextara gave me the product-management foundation: customer discovery, validation, prioritization, 0→1 development, and cross-functional delivery — taking TriageKit from 18 customer interviews to adoption on 6 of 7 new Service Cloud projects.

Operator Experience

Commercial judgment from running a business

I founded and operated ClientCraft, serving 30+ clients and reaching approximately $50K/month in peak agency revenue while owning acquisition, campaign delivery, lead operations, CRM workflows, reporting, and P&L.

AI Solutions Leadership

Leading organization-wide AI implementation

At ServIT, I design and implement AI-enabled operational systems across IT support, recruiting, delivery analytics, and client onboarding for a 40–50 employee organization.

Implementation Discipline

Designed for control, adoption, and business use

My approach includes requirements discovery, solution architecture, workflow controls, human-review checkpoints, exception handling, adoption, and measurable operating outcomes.

About

I’ve worked across product, implementation, operations, and growth.

Product

0→1 development, discovery, roadmap decisions, validation, and cross-functional delivery.

Implementation

AI workflows, automation, RAG, integrations, controls, and deployment.

Operations

Business-process transformation and measurable workflow improvement.

Growth

Customer acquisition, CRM automation, performance marketing, and GTM execution.

My strength is connecting technical AI capabilities to real business workflows — from use-case identification and solution design through implementation, adoption, and measurable impact.

How I work

What I bring into every system

Business problem framing
AI use-case identification
Workflow analysis and process mapping
Solution architecture
AI + automation implementation
Human-review and control design
Cross-functional stakeholder alignment
Adoption and measurable impact

Positioning

I am not trying to be the person who trains foundation models. I am focused on the layer where AI has to become part of a real business workflow.

That means understanding the problem, designing the system, connecting the tools, keeping humans accountable and measuring what changes.

Experience

Jan 2025 — Present

AI Automation & Implementation Specialist

ServIT

At ServIT, I design and implement AI-enabled operational systems across support, recruiting, delivery analytics, and client onboarding — combining automation, LLMs, structured data, integrations, and human review to reduce manual effort and improve speed, accuracy, and decision-making.

System 01

Autonomous IT & DevOps Support Triage

Architected an autonomous IT support triage workflow using n8n, Claude, Jira, and Slack, combining webhook-based intake, intent classification, technical error extraction, knowledge retrieval, automated troubleshooting, and workload-aware routing across 15 support categories.

Recurring tickets automated: 42%; Recurring tickets automated
First-response time: 4 hours → 15 seconds; First-response time
Backlog reduction: 35%; Backlog reduction
Engineering time saved: ~15 hours/week; Engineering time saved
Classification accuracy: 91.4%; Classification accuracy
CSAT: 4.3 / 5; CSAT

n8n · Claude · Jira · Slack · Webhook intake · Workload-aware routing

System 02

Intelligent Talent Matching & Sourcing

Built an automated candidate-matching pipeline using Make.com, LLM APIs, structured JSON extraction, and vector-based matching to parse resumes, score candidates against job requirements, rank profiles, and alert recruiters to high-fit applicants.

Screened per evaluation cycle: 150 applicants; Screened per evaluation cycle
Screening time: 4 hours → 25 minutes; Screening time
Improvement in recruiter acceptance of top-ranked candidates: 38%; Improvement in recruiter acceptance of top-ranked candidates
Time to first interview: 6 days → 3 days; Time to first interview
Processing volume: 3,500+ profiles/month; Processing volume
Uptime: 99.5%; Uptime

Make.com · LLM APIs · Structured JSON extraction · Vector-based matching

System 03

Predictive Project Health & Sprint Risk Analytics

Developed an automated project-health monitoring system using n8n, Zapier, Jira, Git telemetry, and AI-assisted transformation scripts to aggregate delivery signals, calculate sprint-risk scores, and trigger proactive stakeholder alerts.

Precision in identifying delivery bottlenecks: 81%; Precision in identifying delivery bottlenecks
Risk identification: Up to 30 days early; Risk identification
At-risk accounts supported: 8; At-risk accounts supported
Reduction in scope creep / budget variance: 14%; Reduction in scope creep / budget variance
Reporting eliminated per project manager: ~6 hours/week; Reporting eliminated per project manager

n8n · Zapier · Jira · Git telemetry · Sprint-risk scoring

System 04

Automated Document Parsing & Client Onboarding

Designed an AI-assisted onboarding pipeline using Make.com, Zapier, Claude, Jira, and CRM integrations to extract structured data from contracts, SOWs, and intake forms, validate outputs, create operational records, and route edge cases through human review.

Onboarding documents automated: 65%; Onboarding documents automated
Standard setup time: 3 days → under 4 hours; Standard setup time
Data-entry error rate: 8% → 0.5%; Data-entry error rate
Multi-page files processed: 1,200+; Multi-page files processed
Field-level accuracy: 96.2%; Field-level accuracy

Make.com · Zapier · Claude · Jira · CRM · Human review for edge cases

Jun 2021 — Dec 2025

Founder

ClientCraft — Digital Marketing & AI Automation Agency

Founded and operated a performance marketing and automation agency serving 30+ clients, combining acquisition, campaign execution, lead operations, CRM automation, reporting and AI-assisted marketing systems.

Peak agency revenue: approximately $50K/month in peak agency revenue
Meta advertising: managed up to $100K/month in Meta advertising spend across seven concurrent clients
AI-assisted growth systems: lead generation, qualification, personalized follow-up, CRM workflows, scheduling and performance analytics
Client result: helped scale a roofing client from approximately $10K/month to $140K/month within six months using AI-assisted lead generation, CRM automation, follow-up and performance marketing

Jan 2023 — Jan 2024

Junior Product Manager

Dextara Digital

Owned the 0→1 development of TriageKit, a reusable Salesforce Service Cloud case-triage accelerator, from discovery and validation through MVP launch, adoption, and an AI-assisted routing beta.

Product

TriageKit

Reusable Salesforce Service Cloud case-triage accelerator

Product Discovery

Conducted 18 stakeholder and customer interviews using Jobs-to-Be-Done and Opportunity Solution Tree frameworks to identify recurring problems around routing accuracy, assignment speed, setup effort, and post-go-live support.

Stakeholder and customer interviews: 18; Stakeholder and customer interviews
Historical support cases used for concept validation: 25,000; Historical support cases used for concept validation
Routing accuracy demonstrated during pre-build validation: 58% → 71%; Routing accuracy demonstrated during pre-build validation

The concept was validated against historical cases before committing engineering resources.

MVP & Delivery

Prioritized the MVP using RICE and led backlog refinement and sprint reviews across seven two-week sprints with Salesforce developers, QA, design, and solution architecture stakeholders.

Two-week sprints: 7; Two-week sprints

RICE Prioritization · Backlog Refinement · Sprint Reviews · Cross-Functional Delivery

Hybrid AI Routing Strategy

Defined the product approach for a confidence-based hybrid AI workflow that retained deterministic rules for high-confidence cases and used an asynchronous LLM fallback with human approval for low-confidence cases.

The design balanced routing accuracy, latency, cost, and governance rather than routing every case through an LLM.

Product Outcomes

First-assignment routing accuracy: 58% → 81%; First-assignment routing accuracy
Case reassignment rate: 42% → 19%; Case reassignment rate
Median assignment time: 3.2 hours → 12 minutes; Median assignment time
Implementation effort per project: 160 → 55 hours; Implementation effort per project
New Service Cloud projects adopted TriageKit: 6 of 7; New Service Cloud projects adopted TriageKit
Consultant time freed: ~630 hours; Consultant time freed
Paid add-on revenue supported: $18K; Paid add-on revenue supported

Jan 2022 — Jan 2023

Product Management Intern

Supported the Service Cloud product and delivery practice through customer research, requirements documentation, backlog grooming, implementation analysis, and product discovery.

Analyzed recurring delivery and client pain points—including routing setup effort, case reassignment, assignment delays, and post-go-live support issues—to identify repeatable product opportunities that later informed TriageKit.

Stage 01

Discover

Dextara Digital Product discovery + customer research

Stage 02

Build

Dextara Digital 0→1 product development + MVP delivery

Stage 03

Operate

ClientCraft Business operations + growth

Stage 04

Scale

ServIT AI automation + implementation

Capabilities

What I work across

AI Solutions & Implementation

Finding where AI creates real business value — and implementing it

Requirements Discovery
Workflow Analysis
Solution Design
AI Implementation
Solution Architecture
Use-Case Identification
AI Adoption
Process Optimization

Generative AI & Agentic Systems

Designing the intelligence layer, with controls

LLMs
RAG
AI Agents
Agentic Workflows
Multi-Agent Systems
Prompt Engineering
Prompt Chaining
Structured Outputs
Tool / Function Calling
Intent Classification
Knowledge Retrieval
LLM Evaluation
AI Governance
Human-in-the-Loop Systems

Automation & Orchestration

Turning solution logic into repeatable workflows

n8n
Make
Zapier
Relevance AI
Voiceflow
Webhooks
Business Process Automation
Workflow Orchestration
Document Processing Automation
CRM Automation
Reporting Automation
Workflow Monitoring

Business Systems & Integrations

Connecting AI to the systems businesses already run on

REST APIs
LLM API Integrations
Airtable
Supabase
Salesforce
Salesforce Service Cloud
HubSpot
GoHighLevel
Jira
Slack Integrations
CRM Integrations
Data Validation
Structured Data Extraction
JSON Extraction
Document Parsing
Vector Search
Vector Embeddings

AI Product & Delivery

From discovery and validation to shipped, adopted product

Product Management
0→1 Product Development
Product Discovery
Product Strategy
Roadmapping
Product Requirements
MVP Definition
MVP Scoping
Backlog Management
Backlog Prioritization
Agile / Scrum
Sprint Planning
Sprint Reviews
RICE Prioritization
Jobs-to-Be-Done
Opportunity Solution Tree
User & Stakeholder Interviews
Product Metrics
Experimentation
Product Validation
Cross-Functional Delivery

Business, Analytics & GTM

Applied domains, measurement, and commercial experience

Recruiting Automation
Finance Automation
AI-Powered Ticket Triage
CRM Automation
Sales Intelligence
Internal Knowledge Assistants
Predictive Analytics
Risk Scoring
KPI Reporting
ROI Analysis
Cost Modeling
Meta Ads
GA4
Looker Studio
Git Telemetry

Credentials

Education & credentials

Education

M.S., Business Analytics & Intelligence
Stevens Institute of Technology
Jan 2024 — Dec 2025
B.Tech., Computer Science Engineering
Mahindra University
Aug 2019 — Aug 2023

Certifications

Generative AI Leader — Google Cloud
AWS Certified AI Practitioner (AIF-C01) — Amazon Web Services

Contact

Looking for someone who can turn AI ideas into working systems?

I’m interested in roles where AI has to move beyond the demo and become part of how the business actually operates or grows.

Email Me LinkedIn

Open to AI solutions, AI implementation, automation, agentic workflows and AI product opportunities.

harshith.bondugulaa@gmail.com