Embed AI-native product pods directly inside your customer’s team.
With forward deployed software engineers embedded inside Nexsales’ product, data, and GTM teams, Swabhav turned AI ambition into shipped features—without adding management overhead.
Forward Deployed Software Engineers
Tech & AI Consulting 
Talent Practice – High-density engineering pods - B2B MarTech · Revenue & pipeline intelligence
- Global MarTech company · ~150 people (product, data, GTM)
- JavaScript
- TypeScript
- Python
- React, Next.js
- Node.js, Express
- LangChain, LLM tooling
- MongoDB, SQL data warehouse
- Microservices, Docker, Kubernetes
- Cloud-native ML / predictive models, vector search, analytics pipelines
THE VISION
Nexsales wanted to be seen as an AI-native enabler for B2B enterprise clients—not just another MarTech tool. That meant:
- Bringing predictive and agentic AI into their products.
- Prioritising the right accounts and buying signals, at scale.
- Doing all of this without bloating their core engineering team.
Traditional “outsourced development” wouldn’t work. Nexsales needed engineers who:
- Understood their product, customers, and revenue model.
- Could collaborate directly with sales, marketing, and founders.
- Owned AI modules end-to-end—from architecture to deployment—within Nexsales’ environment.
Forward-deployed software engineers from Swabhav became that missing layer: embedded within the team, accountable for outcomes, and measured directly on business impact.
— Founder, Nexsales
BUSINESS IMPACT
— Founder, Nexsales
THE SOLUTION
Role clarity & success blueprint
Instead of “we’ll give you dev capacity,” Swabhav and Nexsales co-defined what success looked like for a forward deployed pod:
- Ship AI features that drive revenue—not just demos.
- Sit inside product, sales, and data conversations, not outside them.
- Own specific modules and outcomes: predictive scoring, agentic analytics surfaces, and AI-assisted workflows.
These were translated into explicit expectations: time-to-first-feature, quality guardrails, and “business impact per sprint” as a shared metric.
Selecting the pod – engineers who can live in ambiguity
From Swabhav’s talent network, a focused pod of engineers was curated and screened for:
- Strong fundamentals across data structures, system design, and cloud.
- Hands-on experience with React, Node, microservices, and modern data stacks.
- Ability to work with non-technical stakeholders and convert fuzzy requirements into clear architecture.
- Comfort with AI tooling and rapid experimentation.
Each engineer was intentionally designed to be part technologist, part product thinker, and part consultant.
Embedding inside Nexsales
- Joined Nexsales’ stand-ups, retrospectives, and roadmap discussions.
- Took shared ownership of modules such as account scoring, agentic analytics, and lead intelligence.
- Worked directly with sales and marketing teams to understand how insights show up in the daily workflows of SDRs and AEs.
This ensured product decisions, model choices, and UX trade-offs were made alongside the teams actually using the system.
Building the predictive & agentic layer
The pod focused on two critical capability tracks:
Predictive AI for account prioritisation
- Unified CRM and engagement data into a clean, account-level view.
- Trained and deployed models to score accounts by conversion likelihood and pipeline depth.
- Surfaced scores directly inside Nexsales’ product so sales teams could focus on the top 20% of high-impact accounts.
Agentic AI for decision support
- Built a conversational intelligence layer pulling governed context across campaigns, CRM, and field data.
- Enabled natural-language questions like “Where should we focus this quarter?” with traceable, action-oriented answers.
The forward deployed engineers owned not just code, but also:
- Tooling and observability for models and services.
- Data privacy, access guardrails, and governance.
- Integration edges into Nexsales’ existing platforms.
Coaching, documentation & handing over capability
The pod was never meant to create vendor lock-in. As systems matured:
- Architecture, runbooks, and pattern libraries were fully documented.
- Engineers paired with Nexsales’ internal team to transfer context and ownership.
- A charter and operating rituals were defined for Nexsales’ first AI innovation squad.
Result: Nexsales didn’t just “implement AI.” They grew an internal team that could think with it.
DEFINITION CALLOUT
WHAT IS A FORWARD DEPLOYED SOFTWARE ENGINEER?
A forward deployed software engineer is a senior engineer embedded directly inside the customer’s environment who:
- Works day-to-day with business, product, and GTM teams.
- Writes production code on real systems—not just proofs of concept.
- Owns outcomes like revenue, adoption, latency, and quality—not just tickets.
- Translates messy, real-world problems into architectures and shipped features.
In Nexsales’ case, these engineers became the bridge between AI strategy and revenue-driving product.
HOW IT FELT ON THE GROUND :
Instead of a distant vendor relationship, Nexsales experienced the pod as an extension of their own team:
- Zero to impact, fast: Engineers contributed to roadmap items within the first few sprints.
- Less friction, fewer hand-offs: Architecture and implementation lived with the same people.
- Higher confidence: Sales and marketing teams could see AI decisions, ask questions, and iterate directly with the builders.