EdgeFirm

EdgeFirm was founded in 2021 by Muhammad Usman Ali and Usman Siddiqui, two engineers frustrated with how traditional consultancies bungled AI projects. The company operates from Karachi, Pakistan and the United States, with a distributed team of 14 engineers: 8 full-stack developers, 4 AI/ML specialists, and 2 DevOps engineers. No salespeople. They've delivered 50+ projects since launch with a stated 50% close rate—they decline work they don't think they can ship in 4–6 months with measurable ROI.

The pitch is direct: you talk to technical founders from discovery onward, not a sales team that hands off to junior developers. Code is production-ready from day one, not a proof-of-concept that lives in a folder. Projects typically finish in 3–5 months. Clients own 100% of the codebase and IP with no vendor lock-in. The co-founders emphasize "over-communicating and under-promising," and their 50% close rate reflects that they'll say no upfront rather than overpromise and miss deadlines.

Services and capabilities

EdgeFirm organizes its work around four core service lines, each with typical 3–5 month timelines and investment ranges.

Decision Intelligence & Analytics: Custom AI pipelines that turn raw data into conversational insights in minutes instead of weeks. They unify data across multiple systems and surface findings through natural-language interfaces. Target clients are financial services, supply chain forecasters, and risk teams. Typical investment is $75K–$150K with claimed 80% reduction in report time and 300–500% ROI in year one.

Intelligent Process Automation: AI agents that handle document processing, customer service, compliance reporting, and cross-system workflows with 95%+ accuracy. The Eona.ae case study (a UAE e-commerce platform) shows this in action: they built a WhatsApp chatbot in 3 months that automated 70% of support queries, reduced response time from 4+ hours to under 30 seconds, and cut support costs significantly. Investment range $50K–$125K; typical delivery 3–4 months; claimed 60–80% reduction in manual work.

Custom LLM Applications: Domain-specific AI assistants using RAG (retrieval-augmented generation), fine-tuning, and agent frameworks. Built for legal research, customer insights, internal knowledge management. Privacy-focused deployment options available. Investment $75K–$175K; 4–5 month timeline; claimed 90%+ accuracy on domain-specific queries.

Data Pipeline Engineering: Unified data pipelines that connect disparate systems, clean messy data, and prepare datasets for AI without disrupting existing operations. Target use cases: multi-system enterprises, M&A integrations, legacy modernization. Investment $50K–$150K; 4–5 month timeline; claimed 70%+ reduction in data errors.

They work with OpenAI (GPT-4, embeddings, fine-tuning), Anthropic Claude, LangChain/LangGraph, and are an AWS Partner with certification. The site lists 30+ RAG systems built on OpenAI and early adoption of Claude 3.5 Sonnet for long-context use cases.

Notable work

Eona.ae (UAE e-commerce/delivery): WhatsApp chatbot delivering 70% support ticket reduction in 3 months, <30 second response times, 24/7 availability, and integration with real-time delivery tracking. Projected 18-month ROI.

Education Tech (unnamed): RAG system processing 50,000+ academic papers, claimed 10x faster research; 4-month delivery.

Marketing Analytics (unnamed): Decision intelligence platform for an agency, claimed 90% faster report generation; 4-month delivery on GPT-4.

Legal Tech (two projects): One for legislative drafting using custom LLM fine-tuning (5 months). Another for consumer legal access claiming 85% cost reduction vs. traditional counsel (GPT-4, 5 months).

HR Technology (unnamed): Knowledge management system unifying Google Workspace, Slack, and company docs; claimed 3x faster onboarding (4 months, GPT-4).

Cybersecurity: In active development—agentic AI platform using Claude Agent SDK to reduce source-code security analysis failures by 80–90%.

Utilities (K-Electric, Pakistan): Mobile platform digitizing billing recovery operations for 2.5M+ consumers; 3 months, Next.js.

Market Intelligence (BWN): Competitive intelligence platform built in 14 weeks using OpenAI.

How they work

Engagement follows a four-phase model: Discovery (define challenges and success metrics), Build (iterative development with weekly demos), Launch (testing, training, deployment), Support (30 days included, optional retainers after). The technical founders own requirements gathering and are directly involved throughout. Project pricing is fixed and transparent; scope is locked upfront to prevent creep. Typical investment starts at $50K and reaches $175K depending on complexity. They maintain 18+ hours daily real-time availability through US operations (EST, CST, MST, PST) and a Karachi development center (PKT), enabling overlap with US mornings. Clients receive complete codebase, architecture documentation, admin credentials, and trained staff—no licensing fees or long-term lock-in.

Team and credentials

Co-founder Muhammad Usman Ali brings 8+ years in full-stack development and DevOps, with expertise in Node.js, Python, React, Next.js, FastAPI, Express, Docker, Kubernetes, CI/CD, PostgreSQL, and MongoDB. Co-founder Muhammad Usman Siddiqui (Lead AI Architect) has 8+ years building AI/ML systems, specializing in LLM architectures, RAG, data pipelines; skilled in Python, Node.js, OpenAI, Anthropic Claude, LangChain, AWS, GCP, and vector databases.

Team: 14 engineers total (8 full-stack, 4 AI/ML, 2 DevOps). AWS Partner with Certified Solutions Architect status and 50+ AWS deployments. Upwork Top Rated Plus with 5-star rating, 100% job success, 50+ projects, $500K+ earned (top 3% globally). Clients in 8 countries. No formal ISO, SOC 2, or HIPAA certification mentioned in source material. 100% client satisfaction reported in post-project surveys.