An AI GTM platform for B2B sales teams and leadership

AI GTM platform development, in this project, meant taking B2Buster’s proprietary consulting methodology and turning it into an intelligent, repeatable workflow. Instead of simply generating text with an LLM, the platform follows a structured research process that collects market signals, researches trusted external sources, and organizes findings into predefined report sections. Euristiq delivered it as a production-ready custom AI solution rather than a proof of concept — combining AI development, architecture design, knowledge base implementation, and product design — so B2Buster became a scalable AI-driven product that generates strategic reports in a fraction of the time.

An AI GTM platform for B2B sales teams is software that automates go-to-market research and analysis — markets, competitors, customer segments, and strategic opportunities — and assembles the findings into structured, decision-ready reports. For B2Buster, Euristiq built one that encodes a proprietary consulting methodology into a repeatable AI workflow: it collects market signals, researches trusted external sources, and organizes findings into predefined report sections, with domain experts kept in the loop to validate insights. The result is a comprehensive go-to-market report in about two hours instead of days of manual analysis.

There is no single best AI platform for market research — the right choice depends on whether you need a generic tool or a system built around your own methodology. Generic tools generate text from the same underlying models everyone uses. B2Buster took a different route: rather than adopt an off-the-shelf product, it worked with Euristiq to turn its proprietary go-to-market methodology into a production-ready AI platform. That approach combines a centralized knowledge base, trusted external sources, and human-in-the-loop validation, so reports stay consistent, factually grounded, and specific to the firm’s expertise rather than generic AI output.

A human-in-the-loop AI platform automates most of a workflow while keeping domain experts involved in decision-making. On B2Buster’s platform, the goal was to automate report generation, but consultants still review generated sections, validate AI-generated insights, provide structured feedback, and improve future recommendations through continuous learning. This hybrid model balances AI automation with human expertise, keeping strategic recommendations accurate, transparent, and actionable — augmenting consultants rather than replacing them.

You reduce AI operating costs in production by optimizing the pipeline, not just the prompts. On B2Buster, Euristiq used tiered model routing on Amazon Bedrock — lighter Claude models for ingestion, mid-tier for research, and the most capable model only for final synthesis — alongside serverless compute that runs only during the bursty report-generation window and throttling-aware retries. Together these cut AI operating costs by roughly five times compared with earlier implementations, without sacrificing report quality, turning an experimental solution into a commercially sustainable SaaS platform.

Timelines vary with scope, but a focused, production-ready AI platform can be delivered in a matter of months when it is scoped tightly and built for production from the start. Euristiq delivered B2Buster’s AI GTM platform in approximately three months — not a proof of concept, but a working platform with a multi-agent pipeline, knowledge base, and human-in-the-loop review — followed by a successful pilot deployment with early customers and positive user feedback.

Agentic AI on AWS means running a multi-agent system — where specialized AI agents plan, research, and coordinate toward a goal — on AWS managed infrastructure. For B2Buster, Euristiq ran a purpose-built agent framework as a container on Amazon ECS Fargate, with a pipeline orchestrator coordinating six pillar research agents, quality gates, and a synthesis stage. The model layer uses Amazon Bedrock with tiered Claude models — Haiku for ingestion, Sonnet for research, Opus for synthesis — while DynamoDB holds pipeline state, S3 stores artifacts, EventBridge Scheduler triggers ingestion, and everything is provisioned with Terraform. Tiered model routing is the primary cost lever.