AI Talent, Built for GTM. Deployed in Days, Not Months.
Nexsales embeds experienced AI and ML engineers directly into your marketing and revenue team — specialists who already understand B2B pipeline systems, not generalists who need six months to learn your domain. You get the AI capability you need, precisely when you need it, without the cost and delay of a full-time hire.
Every Marketing Leader Wants AI Working for Them. Almost None of Them Have the Team to Build It.
The ambition is clear. Predictive lead scoring. Dynamic audience prioritisation for paid campaigns. Automated pipeline forecasting. AI-driven content personalisation. Revenue attribution models that go beyond last-touch.
The blocker is always the same: the people who can actually build these systems are extraordinarily hard to find, expensive to hire, and slow to onboard — especially when your requirements sit at the intersection of AI engineering and B2B revenue operations.
AI initiatives sit on the roadmap for quarters. Vendors are evaluated but never selected. Pilots are launched but never scaled. The competitive advantage of moving first goes to someone else — often a competitor who found a faster path to AI capability than building an internal team from scratch.
Our specialists are already trained in GTM AI use cases. They have already built the models you are trying to build — for B2B companies at scale, across multiple data environments and sales motions. They embed into your team, speak your language, and ship.
Seven Layers of the Enterprise AI Stack. Nexsales Covers All of Them.
Building an AI capability for a revenue organisation is a seven-layer architecture challenge — from raw data ingestion at the bottom to decision intelligence at the top. Nexsales AI specialists have hands-on experience across all seven layers, with the depth to own individual layers and the breadth to architect across the full stack.
The Right Seniority for Every Stage of the Build. Proven Across Real Client Programmes.
Nexsales AI augmentation resources are available across three experience levels — matched to the complexity and stage of your AI initiative, not hired speculatively.
Data cleaning, DB management, data unification and ETL, data quality checks, exploratory data analysis, structured and semi-structured data handling — the foundational layer that every AI initiative needs before modelling begins.
Segmentation and propensity analysis, forecasting, hypothesis design and testing, advanced analytics, statistical modelling, insight generation for marketing analytics — the layer that turns clean data into actionable intelligence.
AI/ML solution architecture, ML Ops workflow design, model development, governance and tracking, deployment and monitoring, system integration, API and connector build — the layer that designs, ships, and scales the AI system end to end.
Predictive ML Engine for Vonage.
Nexsales built a machine learning engine for predicting account-level opportunity conversions using multi-source intelligence — designed to replace intuition-based prioritisation with a validated, data-driven scoring system integrated directly into the sales and marketing workflow.
Started with 200,000 accounts and a 5M+ engagement dataset. After normalisation, exploratory data analysis, and feature engineering — 72 features were derived from the raw signals.
Evaluated five ML algorithm families: Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting. The final model used SHAP analysis to assess lead source effectiveness.
Critically: AI-predicted accounts continued creating opportunities after the outreach campaign ended — demonstrating the model identified genuine buying intent, not just accounts that responded to outreach pressure.
From Scoping Call to Embedded Specialist in Under Three Weeks.
The engagement is designed for speed and specificity — not the drawn-out discovery cycles of a traditional consulting engagement.
One or two calls to understand your use case, tech stack, data environment, and timeline. Nexsales identifies the right specialist profile — experience level, domain background, tooling familiarity — and presents candidates within the first two weeks.
Specialist joins your team's Slack, attends standups, and runs a structured two-week discovery sprint — mapping your data sources, identifying the highest-impact AI use cases, and producing a build plan with delivery milestones.
Active build phase. The specialist ships the first meaningful deliverable within 30 days of the discovery sprint — a working model, a data pipeline, an integrated dashboard, or a deployed agent, depending on the use case.
Engagement continues on a rolling basis, or the specialist runs a structured knowledge transfer to your internal team. Engagements can scale up with additional specialists or wind down cleanly — no lock-in, no minimum terms beyond the initial sprint.
"We had six months of AI backlog and zero internal capacity. Nexsales placed two GTM AI engineers who had our first predictive scoring model live in three weeks. It would have taken us two quarters to hire the equivalent internally — and they still wouldn't have known our domain on day one."