AI systems built for real operations, not demos.
MrZaKaRiA designs and deploys practical AI experiences: internal copilots, chat workflows, automated lead handling, process intelligence, and custom integrations around your business data.
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WHAT WE BUILD
Practical AI, wired into how your business already runs
Not another generic chatbot. MrZaKaRiA designs AI systems around your real workflows — internal copilots, automated lead handling, process intelligence, and custom integrations that read and write to the tools you already use. The goal is measurable operational value: fewer manual steps, faster answers, and a clear path from a working prototype to a system your team relies on every day.
Support, operations, lead flow
Use cases
Prototype to production
Delivery style
API-first
Integration scope
BY THE NUMBERS
What deployment looks like in practice
40
Workflows automated
Across support, ops, and lead-flow engagements shipped to production
120
Hours saved / month
Typical mid-size deployment once the automation is fully adopted
30
Integrations shipped
CRM, ERP, and custom REST/GraphQL APIs wired into live systems
12
Years building systems
Production software experience the AI layer gets built on top of
THE CASE FOR AI
Why teams bring us in
Systems that ship, not slideware
Every engagement is built on real engineering discipline — integrated, monitored, maintainable — so the AI holds up under real usage. You get a partner who has shipped production software for over a decade, not a prototype that impresses in the demo and breaks the first Monday.
0
Years building production systems
CAPABILITIES
What an AI automation engagement delivers
Each capability is deployed against a specific process — scoped, integrated, and measured.
AI automation, wired into the stack
Internal copilot
USE CASE 01
An internal copilot that knows your business
A private assistant that answers from your documents, tickets, and databases — so support, ops, and new hires get the right answer in seconds instead of pinging three people.
USE CASE 02
A lead pipeline that runs itself
Capture, enrich, score, route, and follow up — automatically. The system handles the busywork and hands your team only the conversations that need a human.
Automated lead pipeline
THE AI STACK WE BUILD ON
Model-agnostic and integration-first — the right tool for each layer, wired into your systems.
HOW IT SHIPS
Prototype to production in three moves
A tight, transparent path — you see working software early and often.
We pick one high-value process, map every step and data source, and agree the measurable outcome before a line of code is written.
2
Prototype & data connection
A working prototype connected to your real data — you see it acting on real inputs within the first sprint, not a slideshow.
3
Deploy & iterate
Production deployment with monitoring, guardrails, and a feedback loop. The system improves against real usage instead of assumptions.
DEPTH
Where the engineering is strongest
The disciplines an AI automation project actually leans on.
Workflow automation
LLM integration
API orchestration
Data pipelines
Copilot in the workflow
Automation dashboard
What clients say
Real results from teams who hired MrZaKaRiA to automate a real workflow with AI.
Reviews from live AI automation engagements.
"Our support inbox used to take three people half a day to triage. The copilot now reads every ticket against our own knowledge base and drafts the reply — we just approve. Response time went from hours to minutes."
Yassine B.
Operations Lead, retail group
"I was skeptical of another 'AI tool.' What got built instead reads our CRM, drafts proposals from real client data, and writes the result back in — it's part of how we work now, not a bolt-on app we forgot to open."
Sara M.
Founder, professional services firm
"The lead pipeline automation alone paid for itself in the first month. Capture, scoring, and routing run without anyone touching a spreadsheet, and the handling time is fully visible on our own dashboard."
Karim T.
COO, operations-heavy business
"What stood out was the engineering discipline — usage limits, monitoring, and a real deployment pipeline, not a fragile demo. Our in-app assistant has run in production for months without a surprise bill."
Layla H.
Head of Product, SaaS company
WHERE IT PLUGS IN
Systems the AI already connects to
Slack
Salesforce
HubSpot
Stripe
Notion
Twilio
Zapier
Google WorkspaceSEE IT IN THE WILD
AI woven into real products
FAQ
AI automation, answered
Straight answers for teams deciding whether to invest in a real AI system.
Business-specific systems. A generic chatbot answers from the open web and forgets your business the moment the tab closes. What gets built here reads your own documents, tickets, CRM records, and databases, and is scoped to one real workflow with a measurable outcome — fewer support tickets, faster lead response, less manual data entry — not a demo that impresses once and gets abandoned.
2
Can AI be integrated with the CRM or ERP system I already use?
Yes. Most engagements start there. Your existing CRM, ERP, or internal tool almost always already exposes a REST or GraphQL API, and the AI system is built to read from it and write back into it directly — so results land in the same dashboards and records your team already checks, instead of a separate app nobody opens.
3
How long does a first AI automation take to go live?
A scoped first workflow — one process, one clear outcome — typically reaches a working prototype connected to real data within the first sprint, and a monitored production version within 3-6 weeks depending on how many systems it needs to integrate with. The discovery step exists specifically to keep that scope tight instead of open-ended.
4
What happens to the data the AI system touches?
It stays inside infrastructure you control. The system is built to call your existing databases and APIs directly rather than exporting data into a third-party SaaS platform, and every AI provider call is logged with usage and cost tracking so there's a clear audit trail of what the model saw and generated.
5
Which AI providers or models do you build on?
The systems are model-agnostic by design — commonly Claude or GPT-class models depending on the task and budget — wired through the same integration layer, so switching or mixing providers later doesn't mean rebuilding the workflow around a new API.
6
What does it cost to run an AI system month to month?
Running cost is mostly model API usage plus hosting, which scales with actual volume rather than a fixed license fee. Budget limits and usage tracking are built into the deployment from day one, so cost stays visible and capped rather than becoming a surprise on next month's bill.
