AI Workflow Automation · Since 2012
Hire Pre-Vetted AI Developers & Agentic Systems Engineers.
Custom AI workflows that connect your existing systems, eliminate repetitive tasks, and keep critical business decisions under control.
ISO 27001:2018 Data Secure
ISO 9001:2015 Quality Managed
100% Confidential NDA Assured
SOFTWARE ENGINEERS BUILDING AI-NATIVE PRODUCTS
Timelines depend on scope. We agree a launch date with you after a short discovery call.
Deploy Specialized AI Engineering Talent Tailored to Your Stack
Don’t hire generalists for specialized infrastructure. We provision elite engineers matching the exact architectural layer your product requires.
Product AI & Integration Engineers
- Core Focus: Frontend AI UI/UX, API engineering, model routing, and production latency optimization.
- Technical Proficiencies: OpenAI API, Anthropic/Claude API, Gemini Pro, Hugging Face, TypeSense, Streamlit.
- What they build: Context-aware web interfaces, semantic search features, intelligent customer service layers, and rapid functional MVPs.
RAG & Vector Database Specialists
- Core Focus: Advanced document parsing, semantic search optimization, chunking strategy design, and context window efficiency.
- Technical Proficiencies: LangChain, LlamaIndex, Pinecone, Weaviate, Milvus, PGVector, Qdrant.
- What they build: Enterprise knowledge-grounding engines, compliance document analyzers, and secure proprietary data query layers.
Agentic Workflows & Multi-Agent Architects
- Core Focus: Complex state management, multi-step asynchronous task routing, autonomous execution loops, and custom tool use.
- Technical Proficiencies: LangGraph, CrewAI, AutoGen, Microsoft Semantic Kernel.
- What they build: Autonomous background operations agents, automated code-generation loops, and end-to-end business workflow automation.
What our AI developers build.
From a first chat interface to a multi-agent workflow, our engineers build AI features that run in production, not only in demos.
Customer support assistants that answer from your own documentation, with human handoff
Internal knowledge search over contracts, policies or technical docs (RAG)
Document processing that extracts fields from PDFs and forms, with review queues
Workflow agents that take multi-step actions across your tools, with logging and approval steps
AI features inside existing products, such as summaries, recommendations and smart search
Model cost reduction through prompt caching, smaller models for simple tasks and efficient chunking
PRICING
Every MVP is different.
A simple MVP and a multi-tenant B2B SaaS platform have very different requirements. After understanding your product, we’ll provide a clear scope, timeline, and development estimate.
Stack
A Deep Dive into Our Core Artificial Intelligence Tech Stack
Our developers skip the learning curve. They enter your repository with field-tested expertise in the industry’s leading open-source and proprietary frameworks.
AI Frameworks & Orchestration
Large Language Models & Providers
Vector Databases & Indexing
Observability, Guardrails & MLOps
Blueprint
How We Vet Our AI Engineers: The 4-Stage Technical Screening Pipeline
We eliminate interview fatigue. Only the top 3% of technical applicants pass our rigorous AI sandbox testing protocols to join your project.
Stage 1
Enterprise Engineering Depth Metrics
We evaluate core software fundamentals, data structures, asynchronous pipeline mastery, and advanced Python/NodeJS engineering architectures.
Stage 2
Model Economics & Prompt Optimization Testing
Candidates are tested on real-world constraints: implementing prompt caching protocols, enforcing rate-limit fallbacks, and preventing token bleeding.
Stage 3
The Production Sandbox Challenge
Applicants are given 24 hours to engineer a functional RAG engine from raw, messy enterprise PDFs, measured strictly on data retrieval accuracy and low latency.
Stage 4
Security, Compliance, & Guardrail Verification
Final validation checks the engineer’s ability to safely implement input/output sanitization, PII masking, and structural system defense against injection attacks.
Questions buyers ask
What is the difference between a general software developer and an AI engineer?
A dedicated AI engineer specializes in non-deterministic systems. Unlike traditional software developers who build explicit step-by-step logic, our AI engineers specialize in context injection, managing complex state loops, optimizing prompt token consumption, and building semantic vector indexing pipelines to minimize model hallucinations.
How quickly can a ZonSource AI developer integrate into our workflow?
We can provision and onboard your pre-vetted AI developers within 48 hours. They integrate directly into your existing communication and deployment infrastructure—including Slack, Microsoft Teams, Jira, and enterprise GitHub repositories—matching your team’s active sprint cycles from day one.
How do you protect our corporate data privacy and intellectual property?
Can your developers optimize our API usage and lower model token costs?
Yes. Our technical vetting pipeline strictly evaluates candidates on model economics. Our engineers are highly skilled in implementing advanced prompt caching protocols, routing tasks to smaller open-source models (like Llama 3) where appropriate, and designing efficient semantic chunking architectures to significantly reduce your monthly API billing.
Do you offer a trial period to evaluate the developer's technical velocity?
Yes. We offer a risk-free 1-week sandbox trial phase for all dedicated placement models. This allows your internal technical leads to directly evaluate the engineer’s code architecture, documentation clarity, execution velocity, and cultural fit before committing to a long-term engineering contract.