The NeoAistriq Lab is our R&D engine — where we research, prototype, and productize agentic AI capabilities that become the foundation of every client engagement.
Not a Showcase.
A Workshop.
Most consultancies have a "lab" that's really a marketing page. Ours is where we actually work. The NeoAistriq Agentic AI Lab is an active development environment where we explore frontier agentic capabilities — multi-agent orchestration, LLM reasoning chains, voice-first automation, MCP-connected workflows — and turn the most useful into repeatable frameworks and accelerators.
What gets built in the Lab gets deployed in client engagements. Every IP asset we create reduces your build time and your cost. When we bring a framework to your engagement, you're not paying for us to figure it out — we already have.
Building and testing LangGraph-based agent networks that coordinate across tasks, tools, and data sources. Use cases include operations reporting, lead qualification pipelines, and customer communication workflows.
Exploring Model Context Protocol as a standard for connecting AI agents to business tools — CRMs, project management systems, communication platforms — without brittle custom integrations.
Designing voice-native agentic workflows for SMB environments where keyboard-first interfaces aren't practical — field operations, customer intake, and real-time decision support.
Building reusable measurement layers that connect AI workflow outputs to business KPIs — so every engagement has a live dashboard, not a post-hoc report.
Best-in-class tools selected for capability and ecosystem maturity — not partnerships or certifications. We remain independent of any single vendor.
Our primary reasoning model for agentic applications, chosen for its performance on multi-step reasoning and instruction-following tasks — critical for production agentic systems.
The foundation of our multi-agent workflows. LangGraph specifically enables the stateful, cyclical agent architectures that production agentic systems require.
Model Context Protocol — the emerging standard for connecting AI agents to external tools and data sources. We're building on MCP now to future-proof every integration we deploy.
Cloud infrastructure chosen per engagement based on client environment, data residency requirements, and service availability. We connect to your existing stack — we don't prescribe it.
Every client engagement contributes back to the Lab — anonymized patterns, edge cases, and optimization insights improve the frameworks future clients benefit from. This compounding effect is how a lean team delivers at scale.
We're not starting from zero on every engagement. We're applying accumulated IP and judgment — and getting better with every deployment.