GTM intelligenceCase 01
Killed $2k/mo data subscriptions — fresher data, a fraction of the cost.
$2k/mo → fraction
Subscription cost
60–90d → 7–14d
Data freshness
3–4h → <30min/day
SDR research
PythonClayn8nHubSpot API+2
Build something like thisNewAssociate Consultant @ Motiveminds — open for advisory & builds
AI consulting that ends in a system you actually run.
I'm an AI consultant and engineer. I sit inside your company, map how the work actually happens, decide where AI belongs, pick the models and tools that fit your budget — then build and deploy it.
Process audit → tool & model selection → token economics → shipped system · Bengaluru, IST · remote worldwide
Trusted by teams at
0.0+
yrs
production AI experience
0+
deploys
advised, built, and shipped
0%
reduction
manual ops removed after process audit
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followers
AI community on LinkedIn
Media & Features
Featured by leading AI companies
Podcast guest and featured engineer — talking production AI systems, agentic architectures, and real-world GTM automation.
AI Hero Feature
DataStax·2025
DataStax AI Hero: Sreesha Baraar
Featured by DataStax as an AI Hero — sharing insights on building with Generative AI, favorite dev tools, and advice for aspiring AI engineers.
Watch
Live Session
Setu School·2025
Agentic AI Application Development
Live session with Setu School on building Agentic AI applications — covering multi-agent architectures, LangGraph, CrewAI, and deploying AI agents to production.
Listen
Pick your lane
Same consultant, two ways to work.
Hire a consultant who ships the thing he recommended.
2.5+ years building RAG, multi-agent workflows, and GTM automation that runs in production — now advising on where AI belongs, which tools to buy, and what it costs to run. I don't leave until the team can run it without me.
Download ATS resumeRoles I fit
AI ConsultantApplied AI EngineerForward Deployed AI EngineerAI Solutions ArchitectGTM Engineer / AI GTMFractional / contract AI
Proof
- Associate Consultant — AI Consulting & Solutions @ Motiveminds
- Senior SWE — GenAI @ Motiveminds (enterprise SAP/Salesforce)
- SWE — GenAI/GTM @ W3 SaaS, Dubai DIFC (fintech-grade)
- Full-stack @ Hyderabad Forex (OCR pipeline, regulated finance)
The problem
Most AI projects never make it to production. The rest fail in Q1.
The tool got picked before the process was mapped.
A vendor was chosen in a demo call. Nobody sat with the team doing the work, so the system automates a process that never existed.
The token bill outran the value.
Frontier model on every call, full context every time, no caching or routing. The pilot works — it just costs more than the people it replaced.
Worked in staging, broke on day one.
Pilot looked great in the demo. Then it hit real data, real users, real latency. Production is a different planet.
My lane: consulting that ends in code. I map the process, choose the tools, size the token cost — then build the system, not just the model.
Services
Advisory first, then the build.
Fixed-price or retainer. Every engagement includes a written cost model, deployment, and 2-week post-launch support.
What's included
Embedded AI Consulting
2–4 weeks · $2,500 – $6,000
- Embedded process mapping — how the work really happens today
- AI opportunity map scored by payback, effort, and risk
- Build vs buy vs API call, decided per workflow
- Adoption roadmap + enablement your team can run without me
What's included
AI Cost & Tool Selection Review
1–2 weeks · $1,500 – $3,500
- Token spend teardown per workflow, model, and user
- Model routing: cheap default, escalate only when it earns it
- Caching, batching, context trimming, prompt-cost fixes
- Before/after cost model + budget alerts that hold
What's included
RAG & Document Intelligence
2–5 weeks · $3,000 – $8,000
- Multi-document ingestion + OCR pipeline for scanned intake
- Hybrid retrieval (BM25 keyword + vector semantic)
- Citation-grounded answers + hallucination guardrails
- FastAPI service + Docker deployment, wired into your ops
What's included
Agentic AI Workflow Build
3–5 weeks · $3,000 – $8,000
- Multi-agent architecture (LangGraph / CrewAI / AutoGen)
- Tool calling, planning loops, state mgmt
- Self-correcting workflows with error recovery
- Observability + evals + cost-per-run budgets
What's included
GTM Lead Intelligence Sprint
1–2 weeks · $1,500 – $3,000
- ICP filter design + account criteria
- Sourcing via Clay, Apollo, scraping, Google Maps
- Waterfall enrichment + LLM lead scoring within a set cost/lead
- HubSpot / Salesforce / Airtable handoff
What's included
Fractional AI Consultant
Ongoing · $1,500 – $4,000 / mo
- 8–20 hrs/week depending on tier
- Standing seat in your planning + vendor calls
- Monthly AI spend review + roadmap session
- Priority response within 4 business hours
AI Readiness Audit — entry offer
45-min working session + written roadmap: where AI pays off, which tools, what it costs to run.
$150 – $300
fixed scope
Case studies
Outcomes, not screenshots.
Four production deployments — across lead intelligence, sales automation, enterprise RAG, and regulated finance.
Sales automationCase 02
Decks + personalized email in minutes, not hours.
2–4h → <10min
Time per deck + email
10 → 50+/wk
Outbound capacity
every step → 1 gate
Human review gates
n8nGeminiGoogle Slides APIGmail API+1
Build something like thisEnterprise RAGCase 03
Citation-grounded assistant over hundreds of internal PDFs.
30–90min → <2min
Doc search time
None → 100% cited
Answer grounding
Manual → automated
Cross-doc synthesis
LangChainLlamaIndexFAISSAstraDB+3
Build something like thisRegulated financeCase 04
−40% manual entry, −30% onboarding at a regulated forex firm.
−40% auto-extracted
Manual data entry
−30% faster
Onboarding time
frequent → reduced
Compliance rework
PythonOCR / CVFastAPIREST APIs
Build something like thisSelf-hosted n8n on GCP — production automation backbone
Replaced $500+/mo SaaS subscriptions with self-hosted n8n on GCP — Docker Compose, PostgreSQL, Nginx, SSL. The infrastructure layer behind every GTM/RevOps workflow I ship.
Infrastructure
Docker · GCP · n8n
“I don't start with a model. I start in the room where the work happens — then pick the tools, size the token cost, build the system, and stay until the team runs it without me.”
BBaraar SreeshaAssociate Consultant — AI Consulting & Solutions · Bengaluru
How we work
The engagement, step by step.
01
Discovery call
20 min · free
We discuss your workflow, pain points, and goals. I ask the right questions to understand what's actually broken.
02
Sit in & map the process
3–5 days
I embed with the team doing the work. Every handoff, every workaround, every spreadsheet. You get a process map and a scored list of where AI actually pays off.
03
Tools, models & cost model
2–3 days
Build vs buy vs API, per workflow. Which model at which step, and the token cost per run at your volume. Fixed-price proposal, no hourly surprises.
04
Build, deploy & enable
1–6 weeks
I build with weekly async Loom updates, deploy it, write the docs, and train your team. You own the code. 2-week post-launch support included.
Tech stack
The full kit — picked per job, not per comfort.
Tool selection is half the job. I pick against your constraints — cost, lock-in, latency — not my comfort. Here's the bench.
AI orchestration
LangGraphCrewAILangChainLlamaIndexAutoGenAgent ZeroLangFlow
LLM providers
OpenAI GPT-4Anthropic ClaudeGoogle GeminiAzure OpenAIVertex AIGroq
Vector DBs & retrieval
QdrantPineconeFAISSAstraDBMongoDB AtlasBM25 Hybrid
GTM & RevOps
Clayn8nMakeZapierApolloZoomInfoHubSpotSalesforce
Backend & APIs
PythonFastAPIFlaskRESTWebhooksStreamingStructured JSON
Infrastructure
DockerDocker ComposeGCPPostgreSQLNginxCI/CDGit
Observability, evals & cost
LangSmithLangfuseToken cost trackingModel routingEvalsGuardrails
Data & scraping
Python scrapingGoogle Maps APISQLSnowflakedbtMongoDBOCR / CV
Certifications
LangChain: Chat with Your Data· DeepLearning.AIMulti-Agent Systems with CrewAI· DeepLearning.AIGenerative AI for Everyone· Andrew NgIntro to Generative AI· Google CloudDell Technologies AI-THON· Dell
About
I close the gap between the AI strategy and the running system.
Baraar Sreesha Sreenivas · Associate Consultant, AI Consulting & Solutions · Motiveminds Consulting · Bengaluru, India
Most AI work fails before a line of code is written — the wrong process, the wrong model, a cost curve nobody sized. So I start by sitting with the people doing the work, map what actually happens, and only then decide where AI earns its place.
Then I build it. Cost per run before model selection. Fallback paths before the happy path. Monitoring before the feature code.
Currently Associate Consultant — AI Consulting & Solutions at Motiveminds Consulting. Open to fractional advisory, scoped builds, and full-time AI consulting / Applied AI / FDE roles.
Process audit → AI roadmapModel & tool selectionToken-cost governanceLangGraph · RAG · FastAPI
Based
Bengaluru, India
Community
RAG++ · 3,700+ LI
Experience timeline
Scroll for moreFAQ
The stuff people actually ask.
Writing
View all postsThings I've shipped & learned.
Start here
Not sure where AI fits in your company yet?
A pilot that never shipped. A token bill nobody can explain. Four vendors pitching the same thing. A 20-min call is enough for me to tell you if I can help — and where I'd start.
Bengaluru, India · IST (UTC+5:30)Responds within 1 business day