Get Started with your requirements and primary focus, that will help us to make your solution
Our forward deployed AI engineers join your team, sit with the people who make the decisions and put AI agents into production on your systems. They stay until those agents have earned the trust to run by themselves.
Trusted on five-year contracts by a leading Indian bank and a Dubai government entity.
Forward deployed engineering is a delivery model where software engineers embed inside a customer's team to build, deploy and adapt technology against that customer's real data, systems and workflows. They own the outcome in production, not a specification, and they feed what they learn back into the product.
A forward deployed engineer (FDE) works on site or alongside a customer to turn a business problem into running software. At XenonStack that means mapping decisions, connecting systems of record, building and evaluating AI agents, setting decision boundaries and staying until each agent runs safely in production.
FDEs write production code, but they also sit in the operations review, learn the exceptions nobody documented and earn the trust of the people whose work the agent changes.
| Dimension | Consulting | Staff augmentation | Forward deployed engineering |
|---|---|---|---|
| What you get | A strategy deck and a roadmap | Extra hands working your backlog | AI agents running in production on your systems |
| Who directs the work | The consultancy | Your managers | A shared outcome owned jointly with your business lead |
| Where they sit | Workshops and steering meetings | Inside your IT team | Next to the people who make the decision |
| Platform | None; recommendations only | Whatever your team already uses | ElixirClaw, Context OS and NexaStack, kept after we leave |
| Measure of success | Deliverables accepted | Hours billed | Decisions automated within agreed boundaries |
| When it ends | When the report is delivered | When the contract ends | When your team runs the agents without us |
Every engagement is staffed as a small pod that can take a decision from discovery to production without waiting on anyone else.
Owns the outcome. Embeds with the business, writes production code and runs the rollout.
Builds agents, tools and evaluations. Chooses models per task and policy.
Connects systems of record and builds the context graph the agent decides from.
Writes the decision boundaries, approval rules and trace requirements with your risk owners.
Each stage has an exit test agreed with your business owner. The agent moves on only when its decision traces pass it.
Pod on site. Decision picked, systems connected, agent decides in parallel and nothing executes.
Agent proposes and acts; a person approves each action. Boundaries are tuned from real cases.
Agent acts alone inside agreed limits. Anything outside them escalates with full context.
Full autonomy where it was earned. Your team takes the runbooks; the pod picks the next decision.
Running on your systems at the autonomy level they earned, not stuck in pilot.
Every action recorded with its inputs, policy and approver, ready for audit.
How to operate, monitor, retrain and extend each agent, written for your team.
Your engineers pair with ours until they can run the agents without us.
Need agents built without an embedded team? See our AI agent development services.
Credit, claims, KYC and operations under strict audit. Banking
Citizen services and case work on sovereign infrastructure.
Planning, quality and maintenance from plant and ERP data.
Network operations, service assurance and customer care.
For engineers who want their code to change how a bank, a ministry or a plant actually works, and who would rather be in the room than read about it in a ticket.
You lead a pod embedded with an enterprise customer and own one outcome: AI agents running safely in production.
Locations and on-site travel expectations are listed per opening on the careers page.
An engineer who embeds with a customer to build and deploy software against their real systems and owns the result in production.
Consultants recommend; forward deployed engineers build and run. Success is measured by agents working in production, not by documents delivered.
A first decision typically follows a 90-day path from shadow mode to bounded or full autonomy. Many customers then extend the pod to the next decision.
Yes, wherever it helps. Pods work on site, in your private cloud and remotely, and they can meet data residency and security clearance needs. Specific regions are confirmed per engagement.
Your team keeps the agents, the platform, the decision traces and the runbooks, after a paired handover period.
Strong software engineering, practical LLM and agent experience, enterprise integration skills and the ability to work directly with business stakeholders.
See our open roles on the careers page and apply with a short note about something you shipped that changed how people worked.
Tell us the decision. We'll show you who we'd embed, which systems they'd connect and what the first 90 days would look like.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
