Guide
What is a forward deployed AI engineer?
A forward deployed AI engineer works inside the client's business rather than at arm's length. At Vizabiliti they are experienced business executives with deep AI capability who embed with your teams, run a root cause analysis, then implement an Agentic AI framework and AI-native systems with guardrails, and stay until agreed outcomes are measured.
Forward deployed engineers vs consultants vs off-the-shelf software
| Forward deployed team | AI consultants | Off-the-shelf software | |
|---|---|---|---|
| Where they work | Inside your teams, on site or remote | Mostly outside, through workshops and reports | Not at all; you configure it |
| Output | AI-native systems running in production | Strategy and recommendations | A licence and generic features |
| Fit to your process | Built around your root cause and data | Depends on who implements | You adapt to the product |
| Accountability | Agreed success measures before build | Usually for deliverables, not outcomes | Uptime, not business results |
How a Vizabiliti engagement runs
Engagements follow four stages: Embed, Deploy, Operate and Scale. They start with a 2-to-3-week outcome-based assessment and solutions typically go live in 3 to 8 weeks. Engineers deploy one of six proven products or build a custom product on the same AI core, which provides security, identity, integration and governance from day one. Read about our approach.
Frequently asked questions
What do forward deployed engineers capture?
Business context and goals, role-based functional processes, information flows between teams and systems, and data integrity challenges.
What do they deliver?
A root cause analysis, an Agentic AI framework designed for your enterprise, AI-native systems with appropriate guardrails, and measurable, cost-effective outcomes.
Forward deployed AI vs consultancies vs SaaS
| Vizabiliti (forward deployed AI) | Traditional AI consultancy | Off-the-shelf SaaS | |
|---|---|---|---|
| Time to production | Short phases against KPIs agreed up front; recent builds live in three to eight weeks | Long discovery and recommendation phases; build often handed to another team | Fast to switch on, slow to fit your processes and data |
| Use of proven platforms | Six proven Vizabiliti products plus custom builds on one AI core | Usually builds from scratch or resells third-party tools | One fixed product; gaps left to you |
| Governance and DPDP/GDPR guardrails | Role-based access, separate data per organisation, tamper-evident audit trail, consent management and human review built in | Advised on, then implemented by others | Depends on the vendor; rarely tuned to the DPDP Act |
| Accountability for KPIs | Senior people stay until the agreed business outcomes are met | Accountable for advice, not outcomes | Accountable for uptime, not outcomes |
| Deployment model | Engineers embedded inside your business, working with your teams | Project teams on fixed statements of work | Self-serve or vendor onboarding |
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