AI Agent Operational Lift for Nabler (now Brainlabs) in New York, New York

Why AI matters at this scale

Nabler, now operating as part of Brainlabs, is a digital analytics and conversion rate optimization consultancy. With 201-500 employees and a focus on web analytics, A/B testing, and marketing performance measurement, the firm sits at the intersection of data and marketing strategy. Its clients rely on it to make sense of complex datasets from platforms like Google Analytics and Adobe Analytics, translating raw numbers into actionable business recommendations. At this size, the company is large enough to have substantial data assets and a skilled technical workforce, but still agile enough to adopt new technologies without the inertia of a massive enterprise.

The AI opportunity

For a mid-market analytics firm, AI is not a distant concept—it is an immediate competitive weapon. The core work of pulling reports, identifying trends, and drafting insights is labor-intensive and ripe for automation. By embedding AI into both internal workflows and client-facing deliverables, Brainlabs can dramatically increase the productivity of its analysts while creating differentiated, high-margin service offerings. The firm's existing data maturity—it already deals with structured analytics data daily—means the data readiness hurdle is lower than in many other industries. The primary opportunity lies in shifting from selling hours to selling outcomes, powered by AI.

Three concrete AI opportunities

1. Automated reporting and insight generation. The most immediate ROI comes from using large language models to draft client performance reports. An analyst currently spends hours each week pulling data, formatting slides, and writing commentary. An AI co-pilot connected to client analytics APIs can generate a first draft in seconds, which the analyst then reviews and refines. This can reduce reporting time by 80%, allowing each account team to handle more clients or invest more time in strategic advisory work.

2. Predictive conversion optimization. Brainlabs runs thousands of A/B tests for clients. By training a model on historical test results across its client portfolio, the firm can build a recommendation engine that predicts which test variants are most likely to succeed for a given scenario. This turns institutional knowledge into a scalable asset and improves win rates for client experiments, directly tying AI to measurable ROI in conversion lifts.

3. Natural language analytics interfaces. Embedding a chat-based interface into client dashboards allows marketers to query their data using plain English. Instead of waiting for an analyst to answer “which channel had the highest conversion rate last month?”, a client can ask the dashboard directly. This self-service capability reduces ad-hoc requests and positions Brainlabs as an innovator, potentially unlocking a software subscription revenue stream on top of consulting fees.

Deployment risks and mitigation

For a firm of this size, the biggest risks are data privacy, talent gaps, and client trust. Any AI tool that processes client data must operate within strict security boundaries, with data isolation between clients and no training on proprietary datasets without explicit consent. On the talent side, existing analytics professionals may need upskilling in prompt engineering and model evaluation, but the firm can start with managed AI services to reduce the need for deep ML expertise. Finally, client trust is critical—Brainlabs must position AI as an augmentation that enhances, not replaces, the human expertise clients value. A phased rollout, starting with internal productivity tools before exposing AI to clients, will build confidence and refine the technology safely.