Your custom assistants, built and refined, through real-world experimentation.

☎ Book Strategy Call
☎ Book Strategy Call

See what’s brewing in the lab!

A person writing in a notebook with flowcharts, with a laptop displaying a digital diagram of AI agents, banking API, CRM, and payments, on a desk with a cup, a glowing lamp, and a mug.

Lab Capabilities

Each capability reflects real systems work — designed, tested, and refined through live operational environments.

  • Identify where AI can safely and effectively create measurable impact within your existing workflows.

    Grounded in active lab experiments, this session focuses on identifying high-value opportunities across workflows, data systems, and operations.

    We assess:

    • Where automation can improve efficiency

    • How systems integrate with existing APIs and platforms

    • Risk, compliance, and data considerations

    • Practical pathways from concept to working system

    Outcome: A clear, testable direction aligned to your environment.

  • Design and structure workflows that move from concept to execution.

    This session focuses on translating identified opportunities into practical, working systems. We define how systems operate, how they interact with your data, and how workflows are orchestrated across tools and teams.

    We design:

    • End-to-end agent workflows aligned to real business processes

    • Decision logic, triggers, and handoffs between systems

    • Data flow across APIs, platforms, and internal tools

    • Human-in-the-loop checkpoints for control, quality, and oversight

    • Scalable patterns that can be tested, iterated, and expanded

    Outcome: A defined workflow architecture ready for implementation and experimentation.

  • Architect AI-enabled systems for regulated and high-reliability environments.

    Built on deep experience in financial systems, payments, and enterprise platforms, this work focuses on designing architectures that are secure, scalable, and compliant.

    This includes:

    • API orchestration and service integration

    • Data governance and access control

    • Observability and system monitoring

    • Alignment with compliance and operational requirements

    Outcome: A production-ready architecture aligned with enterprise standards.

  • Deploy and continuously refine AI systems in real operational environments.

    Each implementation is developed as a dedicated system—deployed in an isolated environment with no shared data across organizations.

    This includes:

    • Workflow automation and system development

    • Integration with internal systems and tools

    • Deployment into real-world environments

    • Continuous iteration based on performance and feedback

    Outcome: A working system that evolves through real-world use.

🧪 Lab Activity

  • Empty grid paper notebook page.

    Actively improving AI responses using real coaching workflows, client interactions, and live data. Refined continuously through live usage.

    ⚙️ Live Prompt Refinement Inside HighLevel

  • The image shows a grid with faint green lines on a white background.

    Actively defining secure, compliant AI usage across real banking workflows, with a focus on customer data protection, auditability, and operational trust.

    ⚙️ Privacy-First AI Architecture for Digital Banking

  • Developing a practical AI strategy to support data access, system visibility, and operational resilience across energy environments.

    Developing a practical AI strategy to support data access, system visibility, and operational resilience across energy environments.

    ⚙️ AI Strategy for Modern Energy Infrastructure

Active Experiments

Each experiment represents a working system - continuously tested, refined, and deployed.

Ongoing experiments exploring how AI agents support real operational workflows across finance, infrastructure, and mission-driven organizations.

Scientific Method

Experiments in the Agentic Growth Lab follow a structured scientific method -

observing real operational challenges, forming hypothesis, building AI systems, and testing them in production environments.

Flowchart titled "The Scientific Method Applied to AI Systems" showing four steps: Observation, Hypothesis, Experiment, and Analysis, with arrows indicating process flow and descriptions of each step.

LAB METHODOLOGY

Evaluate how this system could support your organization.

Each implementation is built and deployed in a dedicated environment.

All systems are deployed in isolated environments with no shared data across organizations.

Experiment Log

01 Digital Banking AI Assistant

02 FinTech Incident Response Agent

03 Energy Grid Intelligence

04 Sustainability Impact Agent

05 CRM Workflow Agent

06 Compliance Monitor

07 Energy Data Sharing Agent

08 Growth Funnel Agent

09 Coaching & LifeOps Agent

+ new experiments continuously added

Lab Workflow

Each system progresses through a structured cycle of observation, hypothesis, experimentation, and analysis.

1.

Observation

Identify operational problems or unexplored opportunities where AI agents could create measurable impact.

2.

Hypothesis
Define a testable approach — mapping systems, APIs, data flows, and workflows to determine where AI can operate effectively, safely, and deliver the highest value.

3.

Experiment
Build and test early agent workflows designed to automate monitoring, analysis, reporting, or decision support in real-world environments.

4.

Analysis & Evolution
Use data to evaluate results, refine system performance, and continuously evolve deployments through iteration and real-world feedback.

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Let’s Work Together

If you're interested in working with us, complete the form with a few details about your project. We'll review your message and get back to you within 48 hours.


Agentics Growth Lab was founded by fintech product leader Nicole Chernow-Martinez to experiment, ship, & grow practical AI systems across financial infrastructure, energy systems, and automation.