Awais Ali, back to the opening scene

Awais Ali

Solution Architect & Software Engineer

Co-founder of an engineering company · 15+ years · 100+ engineers led

I build Gen AI systems, AI agents, .NET platforms, mobile apps, ERP and CRM, GIS platforms and UAV systems.

I lead the design and build: mission and UAV platforms, digital twins, enterprise SSO, AI agents, ERP and CRM.

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Domains

Where the
work runs.

Every domain, one way of working: designed to be understood, operated and changed.

  1. Enterprise & apps
  2. Data platforms
  3. AI & agents
  4. Identity & security
  5. Blockchain & Web3
  6. Geospatial & C2
  7. UAV & autonomy
Illustrative overlays on photographs, not product screenshots.

How a system grows from zero to 1M+ users

From zero to scale

The stages a system meets on its way from its first user to its millionth, each with a pattern from my own work. The architecture changes at every one.

  1. 0 · Prototype — One service on a shared Web API framework, one database.
  2. 1 · Launch — Real users arrive; updates reach them live over SignalR.
  3. 100 · Operate — Agents join the services; evals decide which changes stay.
  4. 10K · Optimise — Sign-on moves to Kubernetes to take surges at game launches.
  5. 100K · Scale — A whole country's maps, tiles and search ship as offline packages.
  6. 1M+ · Sustain — Several users share one scenario, each view filtered by clearance.
Reference architecture Clients (web, mobile, devices) connect through an API gateway to backend, services and agents, which share a data layer backed by cache, queue and storage, all running on shared infrastructure. An AI layer is wired in beside them: the gateway reaches an LLM gateway behind guardrails; an agent orchestrator works with the agents, calls the LLM gateway, a tool registry (MCP) and a vector store (RAG) beside the data layer; evals and observability sit beside the platform. ClientsGatewayCapabilitiesDataStoragePlatform Web Mobile Devices API gateway Backend Services Agents Data Cache Queue Storage Infra LLM gatewayLLMgateway Guardrails Agent orchestratorAgentorchestrator Evals · observabilityEvals ·observability Vector store (RAG)Vector store(RAG) Tool registry (MCP)Tool registry(MCP) AI layer
Reference architecture

Night lights: NASA Earth ObservatoryLights: NASA

Selected work

All work

Systems,
not slides.

years in practice
15+
projects in total
100+
countries with projects
7
users across projects
1M+
uptime, SSO platform
99.9%

Air-gapped mission platform⁠ — case study

Geospatial & C2 · Data platforms · Solution architect & engineering lead (10-person team)

Contains modified Copernicus Sentinel data

Illustrative concept, not a product screenshot

Also on this map: Country-scale data pipeline, Multi-UAV mission control, Custom UAV platforms, Perception and autonomy, Long-range antenna tracking and multi-link communications

A country-scale, fully offline mission simulation and command-and-control platform that generates its 3D scene at runtime from what operators draw on a 2D tactical map, so formations drawn in 2D play out as those formations in 3D, on a timeline.

person team
10
feature types
100+
FPS
30+

The challenge

The platform simulates missions and visualises command and control across a whole country, and it runs with no network at all. Its core is runtime generation: real-time 2D tactical drawing drives a 3D terrain engine, so formations drawn on the 2D map play out as those formations in 3D, on a timeline.

What I did

  • Generating the 3D scene at runtime from the 2D drawing
  • Sharing each saved scenario by clearance, decided in an admin panel

Stack

The result

  • The platform was delivered end to end into high-security offline sites, from the servers to the projection hardware.
  • It generates 100+ feature types from the 2D drawing at runtime and sustains 30+ FPS in 3D.

Agentic AI systems⁠ — case study

AI & agents · Enterprise & apps · Architect & builder

Illustrative concept, not a product screenshot

Agent systems I build and run daily: an intelligence-triage agent with eval-driven loops, a self-hosted multi-agent assistant, an agentic document pipeline and a two-model agentic engineering workflow.

agent systems in daily use
4

The challenge

Agent output has to be checked before anyone relies on it, and an agent's answers must come only from a known record, not from what the model happens to say.

What I did

  • Eval-driven loops in the triage agent
  • One model builds while another reviews, and the roles swap by task.

Stack

The result

  • There are 4 agent systems in daily use.
  • A change to the triage agent is kept only when the evals say it improved.
  • Nothing the document pipeline drafts is sent without a human review.
  • The chatbot on this site answers from its own content.

Capabilities

Across the stack.

The full stack

About

Solution architect and software engineer. I design systems and build them with the team.

A former licensed pilot who builds the drones as well as the software that flies them. Based in Lahore, UTC+5.

More about me

Terrain: public Terrarium elevation tiles over the Karakoram, chosen for relief.

Contact

Let's build it
now.

Available for solution architecture, technical leadership and hands-on builds.

Lead the architecture of a new platform, review the one you run, or design and build a system end to end with your team.

Lahore, Pakistan (UTC+5) · remote or hybrid

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