Pipeline growth
0×
+208% vs. manual baseline
AI Automation Specialist · Islamabad, PK
Ship AI systems that eliminate manual work, not add to it.
I'm Shahvaiz Ahmed, I build the quiet infrastructure that makes teams faster. AI lead scoring, RAG chatbots, workflow automation, and recommendation engines. Production-ready, measurable, and owned by you in 90 days.
- Python
- Django
- React
- Next.js
- AWS
- LangChain
- OpenAI
- n8n
- Airflow
- Snowflake
- PostgreSQL
- Pinecone
- Terraform
Impact metrics across recent engagements
Auto-resolved tickets
0%
+340% deflection lift
Revenue lift
0%
8-week A/B test
Hours saved / week
0+
per ops team
Four AI automation services. One outcome.
Every engagement ends with less manual work, more predictable output, and a team that understands how the system works. Built by an AI developer with 4+ years of production Python and 2+ years of LLM infrastructure shipping.
AI lead generation & qualification systems
Custom scoring engines that read your CRM, enrich leads against 10+ data sources, rank them on fit and intent, and draft first-touch outreach, so sales receives only the top 10% with evidence attached.
Replaces: manual research, gut-feel prioritization, cold list-building. Typical build: 4–6 weeks.
- Python
- GPT-4o
- Clay
- HubSpot
- Postgres
Intelligent chatbots & retrieval-augmented AI
Production RAG chatbots grounded in your docs and ticket history. Deflect 60–80% of L1 support volume, escalate the rest with full context and suggested replies. No prompt drift, evaluation harness included.
Replaces: L1 support overload, repetitive internal questions, FAQ drift. Typical build: 3–5 weeks.
- LangChain
- Pinecone
- OpenAI
- Intercom
Workflow automation pipelines with n8n & Python
n8n and Python pipelines that bridge your tools (CRM, helpdesk, billing, analytics) and eat the 50 operational tasks nobody wants to own. Ships with monitoring, alerting, and rollback baked in.
Replaces: data entry, manual report pulls, cross-app glue work. Typical build: 2–4 weeks.
- n8n
- Airflow
- AWS Lambda
- Prefect
Predictive analytics & recommendation engines
Recommendation and forecasting models embedded directly inside your product surfaces, checkout, email, in-app, not a separate dashboard that nobody checks. A/B tested with honest confidence intervals.
Replaces: flat UX, guesswork roadmaps, out-of-context analytics. Typical build: 6–10 weeks.
- PyTorch
- dbt
- Snowflake
- React
Selected wins.
Three recent AI automation engagements with clear before/after measurements. Scope, stack, and outcomes on the record.
Case 001 · 2025
3×
B2B SaaS · Series AAI lead scoring system
Tripled qualified pipeline in 90 days, without adding SDRs.
Built a scoring engine that ingested 40,000 raw leads, enriched them against 12 data sources (Clearbit, LinkedIn Sales Nav, BuiltWith, firmographics, technographics, and more), and delivered sales a ranked shortlist with evidence attached. Replaced approximately 2.5 FTEs of manual research. Demos booked tripled in 90 days, and SDR time-to-first-touch dropped from 4.2 hours to 11 minutes.
- Python
- GPT-4o
- Clay
- HubSpot
- Postgres
Case 002 · 2025
80%
E-commerce · Scale-upRAG support chatbot
80% of L1 tickets resolved without a human ever seeing them.
Retrieval-augmented chatbot trained on 3 years of support tickets and product documentation (14,200 docs indexed). Handles 80% of Level-1 volume end-to-end and, when it escalates, it hands the human agent full conversation context plus a suggested reply. Average handle time reduced by 46%, and the support team reclaimed their nights and weekends.
- LangChain
- Pinecone
- OpenAI
- Intercom
Case 003 · 2024
34%
Retail SaaSRecommendation engine
34% revenue lift, measured against the existing stack.
Hybrid collaborative-filtering + content-based recommendation model embedded into the checkout flow and lifecycle email. Rigorous A/B tested for 8 weeks with confidence level p < 0.01. The revenue-per-visitor lift held at 34% at full rollout and has continued to compound as the catalog grows.
- PyTorch
- dbt
- Snowflake
- React
What clients say.
Short, on-the-record feedback from three recent engagements, the full case studies are above.
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"Shahvaiz rebuilt our lead pipeline from a 40k-row spreadsheet into a scoring engine our reps actually trust. Demos booked tripled inside a quarter, without adding SDRs."
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"The RAG chatbot went from sketch to production in five weeks and now resolves 80% of L1 on its own. Our support team got their weekends back, genuinely."
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"A/B tested, shipped, and held at +34% revenue per visitor for eight weeks. The model is inside checkout and lifecycle email, not sitting in a dashboard nobody opens."
How we'll work.
A tight 4-phase engagement with measurable gates. No open-ended retainers, no mystery billing, no vendor lock-in.
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Map & audit
Week 1. I shadow your team, map every manual task, cost it in hours + dollars, and rank automation opportunities by ROI. Deliverable: a scored backlog so decisions are grounded in data, not opinion.
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Prototype
Weeks 2–3. The highest-leverage system ships to a sandbox environment with real data and a clear performance baseline. You see the thing running, not a slide deck about the thing.
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Production
Weeks 4–9. Rollout with observability, alerting, rollback, and a "beats baseline" acceptance gate. If the system doesn't measurably outperform the baseline you saw in the sandbox, production fees are waived.
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Hand-off
Weeks 10–12. Documentation, runbooks, Loom walkthroughs, and 60 days of post-launch support. Your team owns the system, no lock-in, no surprise costs when the knowledge leaves the building.
About
Hi, I'm Shahvaiz.
I'm a software engineer turned AI automation specialist based in Islamabad, Pakistan. I've spent the last four years shipping production systems in Python, Django, React, and AWS, first as a software engineer, then as a senior engineer and lead, and now building AI automation as my entire practice.
The shift happened because I kept watching teams drown in manual work that their codebase couldn't fix. Sales reps qualifying 500 leads by hand. Support copy-pasting answers from Notion. Operations pulling the same report every Monday at 6 AM. The problem wasn't ever the code, it was everything happening around the code. So I made a shift: from building features to building freedom.
Today I work with SaaS founders, enterprise teams, and government agencies who are ready to scale without scaling headcount. If your team is spending more time on manual work than strategic work, we should talk.
Frequently asked questions about AI automation.
Common questions from SaaS founders, engineering leaders, and operations teams considering an AI automation engagement.
What does an AI automation specialist do?
An AI automation specialist designs and ships production systems, lead scoring engines, RAG chatbots, workflow pipelines, recommendation models, that replace repetitive manual work in sales, support, and operations. The measurable deliverable is typically 20+ hours per week reclaimed per team, plus revenue or pipeline lift in the 20–200% range depending on the use case.
How long does it take to build an AI chatbot for customer support?
A retrieval-augmented (RAG) support chatbot grounded in your documentation and past tickets typically ships to production in 3–5 weeks. Week 1 is discovery and data preparation; weeks 2–3 build the retrieval + generation layer with an evaluation harness; weeks 4–5 cover guardrails, escalation flows, and rollout. Deflection rates of 60–80% of L1 volume are common in the first quarter.
How much does AI automation cost for a SaaS company?
Typical project budgets:
- $18k–$28k, single automation (lead scoring, support chatbot, or a workflow pipeline).
- $60k–$120k, full platform with multiple systems, monitoring, and hand-off.
- $100–$800 / month, ongoing AI infrastructure (models, vector DB, retrieval).
Every engagement includes a fixed-scope statement of work. No open-ended retainers.
What tech stack do you use for AI automation?
Primary stack: Python (Django, FastAPI), React / Next.js, AWS (Lambda, S3, RDS, Bedrock), n8n and Airflow for orchestration, LangChain + OpenAI / Anthropic for LLM work, Pinecone or Turbopuffer for vector search, and Snowflake + dbt for analytics pipelines. All infrastructure deployed with CI/CD and monitoring baked in via Terraform.
Do you work with enterprise or government teams?
Yes. Current and past engagements span SaaS scale-ups, enterprise e-commerce, and government agencies. Enterprise work includes strict data residency, SSO, audit logs, and SOC 2–aligned deployment patterns. Government engagements run on air-gapped or in-country infrastructure when required.
What's the process for starting an AI automation project?
A four-phase engagement with measurable gates:
- Map & audit, I shadow your team for a week and rank every manual task by ROI.
- Prototype, the highest-leverage automation ships to a sandbox in weeks 2–3.
- Production, rollout with monitoring, rollback, and a "beats baseline" gate.
- Hand-off, docs, runbooks, walkthroughs, and 60 days of support.
Can I hire you for AI consulting in my timezone?
Yes. I'm based in Islamabad, Pakistan (PKT, UTC+5) and work across US, European, and Asia-Pacific timezones. Typical availability overlaps at least 4 hours with every major business timezone, and async-first collaboration with written daily updates is the default.
Do you provide ongoing support after the build?
Every build ships with 60 days of post-launch support included. After that, you can extend with a lightweight retainer (4–8 hours / month) if you'd like me on call for tuning, new features, or model upgrades, but most teams don't need it after month three. You'll own the code, the docs, and the runbooks from day one.
Ready to replace the manual work?
Send me the one workflow costing your team the most hours per week. I'll tell you, honestly, whether automation is the right move and roughly what it would take.