I build AI systems teams can actually run. Multi-agent workflows, RAG, voice agents, and production automation for messy ops problems.
Fewer notebook demos. More approval gates and things you can click. Start with Business OS, the flagship. I also build AI voice agents that turn a missed call into a booked job. Proof: daytime · after hours.
Watch the walkthrough, open the live app, or expand a product screenshot. Multi-agent system with dense RAG vaults, not a naked chat wrapper.
Product walkthrough
~2 min · strategist → plan lock → specialists → outputs
Multi-agent · Production · 2026
Behind the scenes
Most demos bolt a model onto a thin prompt. Business OS is built so the strategist and copy specialist pull from curated corpora with real depth: frameworks, playbooks, templates, and checklists you can measure. That is what makes plan lock and specialist drafts feel like implementation work instead of generic AI chat.
Strategist knowledge
The Business Strategist retrieves from a structured Hormozi corpus: offers, leads, money models, and growth playbooks. Not a thin prompt. A real knowledge base the plan is supposed to cite.
3
core books
12
implementation playbooks
Dozens
lead, sales, retention systems
Domains: grand-slam offers, lead machines, sales and retention systems, profit models.
Copy specialist knowledge
When a plan routes to Copywriting, the specialist is grounded in a high-density copy vault. Frameworks, templates, checklists, and real swipe examples, not freeform model prose.
14
in-depth vault docs
112
named frameworks
32
structures and templates
58
checklists and QA
600+
swipe and example files
40+
authors and sources
Sources span classic and modern persuasion (Hopkins, Schwartz, Cialdini, Ogilvy, Kennedy, Brunson, Hormozi, and more).
If it has a UI or canvas, you get a snapshot, and a live link when one exists. Voice agents are the other proof: the business math and the live call, both on YouTube. If it only ran in the background, I explain how it worked. No fake product screens.
A live multi-agent product. You bring a messy problem; it locks a plan, runs specialist agents, and hands back paste-ready work.
Missed HVAC calls become a booked job. After hours, a tonight page or the first morning truck. At lunch, a callback or a held window. Both clocks are on YouTube.
Business outcome
How it is built
Outbound in one canvas: enrich the agency, scrape Apollo, create Instantly campaigns, verify email.
Inbox in, summarize, classify, draft from your docs, review, then send. Grounded email automation.
New CRM contact, scrape social, merge profile data, AI-personalize, send from Gmail.
Sheet of leads in, company research agent, AI sales draft, send when you trigger the link.
Shadow and supporting systems
Plenty of agentic and ops work never gets a pretty product page. It fires on events, writes logs, and messages people in tools they already use. These cards document that work as it actually ran.
Revenue protection at the meeting layer. Score no-show risk, pick channel and timing, follow up without someone babysitting every booking.
Problem
Booked sales calls no-show. Generic reminders ignore risk, timing, and channel. Every missed meeting is revenue you already paid to book.
How it works
This was never meant to be a product people open. It sat behind the calendar. When a meeting was booked, the agent scored how likely the lead was to no-show (history, source, lag to meeting, incomplete data). High risk got earlier or multi-touch reminders; low risk got a light touch. Messages went out through the usual messaging tools, and every decision landed in a run log so ops could audit later.
How it connects
Calendar or booking source, agent runtime, LLM scoring, SMS/email providers, then a spreadsheet or CRM log. Classic ops agent shape: event in, decision out, side effects on channels, no end-user dashboard.
Where it ran
Background agent in a Hermes environment with model APIs and calendar/message integrations. No public URL. Hermes is off my stack now; the design and loop still stand as implementation work.
Why no product screenshot
There was no single product screen. The UI was calendar notifications and messages people already get. Control plane was agent config and logs. After retiring Hermes, there is no live instance to open, so this card explains the architecture instead of faking a mockup.
Autonomous outbound factory for Fractional CFOs. Golden Sheet and GDoc cloning at scale on Modal and Google Workspace, quality-first Profit Gap assets.
Problem
Spray-and-pray outbound kills boutique trust. fCFOs need hyper-personalized magnets and campaigns without a $25k/mo retainer.
How it works
Antigravity is the agent persona that runs Agency OS. Always read AGENTS, STATUS, and MEMORY before work. Production scripts (modal_verified_runner_turbo, verified_asset_generator) clone golden templates, personalize per lead, and write campaign-ready assets. Auth tokens refresh on a Modal volume shared by runners, never baked into images.
How it connects
Apollo-class lead sources into a Python/Modal factory, out to Google Sheets and Docs, then campaign ESP handoff. Agent skills cover lead generation, asset cloning, and session protocol.
Where it ran
Modal cloud for batch generation; Google Workspace as the asset plane; agent workspace on OpenCode/Antigravity. Public docs and scripts: github.com/Deekshak11/agency-os. Private full backup: Agency_OS.
Why no product screenshot
The product surface is batch jobs, Google assets, and agent context files, not one app window. Open GitHub for architecture, SOPs, and runners. The portfolio explains the system without leaking client lead data.
Always-on agentic infrastructure: Mission Control, Hermes runtime, Hindsight memory, hourly GitHub DR backups. First wedge was Show-Rate Guardian.
Problem
Solo operators need 24/7 agent ops with memory, skills, and restore-from-wipe resilience. A chat tab that forgets everything is not enough.
How it works
Signal OS is the always-on layer under product skills. Hermes (or equivalent) runs around the clock. Mission Control surfaces chat, kanban, and memory. Hindsight holds long-term graph memory. Skills like Show-Rate Guardian plug in as operator-facing wedges.
How it connects
Agent runtime, skills (Show-Rate and others), tools and APIs, Hindsight plus session capture, GitHub backup for disaster recovery.
Where it ran
Docker Compose historically on a VPS; public docs on github.com/Deekshak11/signal-os. Full runtime state stays in a private Agentic-OS backup.
Why no product screenshot
Ops surface is Mission Control and agent sessions, often private. Architecture docs and Show-Rate skills on GitHub are the hire-facing proof.
Manual ops turned into composable workflows with retries and dead-letters. The boring reliability layer agents need later.
Problem
Hours lost to routing, notifications, and CRM upkeep. Early automations broke silently when APIs failed.
How it works
Classic iPaaS automation: event or schedule fires, transform, call systems of record, branch on status, retry transient errors, park poison messages. No LLM required. This is the boring half of production AI that keeps agents from becoming expensive chaos.
How it connects
Forms, CRMs, email, Slack/Teams, sheets, whatever ops already used. Workflows sat between SaaS tools as glue. Humans only touched exceptions.
Where it ran
n8n / Make / Zapier cloud (and later self-hosted n8n patterns). Credentials live in the automation platform, not in a custom app. Many flows were internal with no public domain.
Why no product screenshot
Dozens of small flows, not one flagship UI. Screenshots of client CRMs or private workspaces are not portable. The portable proof is the pattern: retries, dead-letters, explicit failure paths, described here.
Independent GTM and engagement loop for a growth offer: landing, research, content system. Not employment.
Problem
Fragmented positioning and no reliable path from stranger to qualified interest.
How it works
Not an agent product. A GTM loop. Research clarified who the offer was for. Landing converted cold traffic. Community and content kept prospects warm and filtered tire-kickers. Independent collaboration, not a W-2 role.
How it connects
Site or landing, analytics, content community, then manual or light-automated follow-up. Optional later glue to CRM and email tools.
Where it ran
Marketing site plus community platform (Skool-class). Standard web hosting. No custom agent backend.
Why no product screenshot
Client and collab surfaces and private community spaces are not ideal portfolio assets without clearance. The hire signal is the GTM loop design, stated clearly.
Fast AI-assisted marketing sites for SMBs. pantherfitness.in and k2salon.in, with local brand search strength.
Problem
Local businesses needed findable conversion sites without agency timelines or retainers.
How it works
Standard web delivery, sped up with AI-assisted build tools, then finished as real public sites. Focus was speed-to-live and local discoverability, not a custom backend.
How it connects
Standalone marketing sites. Optional Google Business and analytics. Not wired into the later agent stack.
Where it ran
Public web hosting for each client domain (for example pantherfitness.in). Open live where links still work.
Why no product screenshot
Live sites are the proof. Use Open live. No need for a frozen PNG when the real property is one click away, and client pages change over time.
Deekshak SS
AI implementation · Orchestration
Target
AI implementation
Mode
Remote
Proof
Live products
I look for roles where the brief is fuzzy and someone has to own the full loop. Find the real problem, wire models and tools, add human approval where it matters, and ship something ops can run.
Business OS is the flagship proof: strategist RAG on a dense Hormozi vault, copy specialist on a full Copy OS corpus, plan lock, live at app.deekshak.site.
I also build AI voice agents, and the proof is a business number plus the system that produces it. On a $1M HVAC shop, after-hours voicemail keeps about 6% of ~60 calls a month. The agent in the film offers a $189 tonight page or the first morning truck, and that capture math is about $90k a year. The daytime film is lunch and overflow: voicemail keeps about 17 jobs (~$8k); the same agent keeps about 130 (~$60k). Under the call she says she is the after-hours AI, an n8n webhook checks the calendar, and the tech gets the job while the caller is still on the line. Daytime · after hours. Around that sits meeting show-rate work, lead pipelines, and the reliability layer those agents need.
Stack is a means, not a brand. Agents, RAG, n8n-class automation, Modal, Vercel, whatever gets a production system online. If you evaluate on demos more than buzzwords, we should talk.
I keep formal courses light on purpose. Structure is useful, but the reality check of what works comes from building, shipping, iterating, and taking market feedback. Credentials are listed under Credentials. The portfolio above is still the main proof.
No skill bars. These show up in the case studies, not as a laundry list of every tool I ever touched.
A short stack of verified courses. Secondary to live systems and case studies above.
I do not stack courses for the sake of a longer resume. Structured learning helps with vocabulary and frameworks, but the market only cares what survives contact with real workflows, bad data, and users. That feedback comes from building, shipping, iterating, and putting work in front of people. Credentials below are useful baselines. The case studies and live demos are the proof.
How I approach AI implementation on a team. Not a consulting sales funnel.
Find what's actually broken, not only what was asked for.
Cheapest path that could work under production conditions.
Approval gates, retries, observability. Silent failures are bugs.
Deploy, demo, and map it to a number the business already tracks.
What you get on an AI implementation team: ownership of outcomes, not a tool list.
Live products and workflows with gates, retries, and demos. That is the bar for implementation roles.
Diagnose, prototype, harden, measure. Silent failures are bugs, not model quirks.
Every build maps to a metric teams already track: show rate, missed-call recovery, lead quality, time to artifact.
Best fit is remote AI implementation, agent orchestration, or automation engineering. Roles where someone owns the loop from a fuzzy brief to a system that actually runs in production.
Available remote, India and international. Prefer teams that evaluate on demos and ownership, not keyword bingo.