Forward-Deployed AI Engineering - Production AI Systems for SMBs

Forward-Deployed AI Engineering

Working AI systems,
not strategy decks.

We embed engineers with your team, map the real workflows, build production-grade AI inside your existing stack, and leave your people able to run it. No vendor lock-in, no chatbot demos, no rip-and-replace.

Assess Your First Workflow See Engagement Options

The problem

AI pilots are easy. Production adoption is the hard part.

Most companies have tried AI by now. A chatbot. A summarization tool. A pilot that worked in a demo and stalled the moment it touched real data, real approvals, real users, and real cost.

The gap between a working demo and a system your team actually depends on is almost entirely operational:

Messy, fragmented data spread across SaaS tools that were never designed to talk to each other

Undocumented workflows that live in someone's head, not in any system of record

Internal teams already at capacity, with no slack to take on a new platform

Governance, security, cost, and ownership questions nobody has answered yet

That gap is where Forward-Deployed AI Engineering lives.

What an FDE is

An engineer who ships, not a consultant who decks.

A Forward-Deployed Engineer (FDE) embeds with your team, maps the real workflows, builds production-grade AI-enabled systems inside your existing stack, and leaves reusable operating capability behind.

The deliverable is a system your team owns and runs — with code in your repo, monitoring in your dashboards, and people who know how it works.

The Pavise method

How an engagement actually runs

1

Embed

We sit with the people who do the work — not just the people who manage it. We watch the actual clicks, the spreadsheets, the swivel-chair integrations, and the exceptions. Real workflows first, slide ware never.

2

Select

We pick one workflow where AI creates real leverage — measurable time saved, error rate dropped, or capacity unlocked — and where the data and ownership exist to support it. We are explicit about what we are not building and why.

3

Build

We ship a production-grade AI-enabled system inside your existing stack. Your repos, your identity, your data boundaries. Code you own, config you control, deployment paths you can re-run.

4

Govern

Access controls, auditability, evals, cost monitoring, rollback, and human-in-the-loop review are part of the build — not an afterthought. Security-minded by default, because this is a Pavise engagement.

5

Train

Runbooks, documentation, and handoff sessions. We name internal champions who can operate, troubleshoot, and extend the system. The goal is your capability, not our dependency.

6

Compound

The first system is a pattern. The next workflow is faster because the guardrails, eval harness, and operating muscle already exist. Each build makes the next one cheaper and the internal team stronger.

Engagement options

Four ways to start

Pick the shape that matches where you are. Most companies start with an Assessment and move into a Build Sprint.

1-2 weeks Fixed scope

AI Acceleration Assessment

A focused diagnostic that tells you exactly where AI will and will not help — before you spend engineering budget.

You get:

• Workflow map of where time and errors actually accumulate
• Opportunity backlog ranked by leverage, effort, and risk
• ROI and risk matrix for each candidate workflow
• Data readiness review — what exists, what's clean, what's missing
• Recommended first production use case with a clear rationale
• A do-not-build list — the workflows that will waste your money

Start an Assessment

MOST COMMON

2-6 weeks Production output

Forward-Deployed Build Sprint

We build one production-grade AI-enabled workflow, end to end, inside your stack — and hand it over ready to run.

You get:

• One production AI-enabled workflow live in your environment
• Code and configuration in your repository — you own it
• Documented deployment path you can re-run without us
• Evaluation and test harness so quality is measurable, not vibes
• Monitoring and cost controls wired into your observability
• Runbooks and handoff so your team can operate it day one

Scope a Build Sprint

Monthly Fractional

Embedded AI Engineering Retainer

Fractional embedded AI and platform engineering capacity that compounds across multiple workflows over time.

Best for:

• Companies that have validated AI value and want a steady build cadence
• Teams without a dedicated AI platform function who need senior capacity
• Iterating on a second or third workflow once the pattern is proven
• Scaling internal champions with embedded engineering mentorship

Discuss a Retainer

Ongoing Operate

Managed AI Operations

We keep production AI systems healthy after launch — the operational layer most teams underestimate.

We cover:

• Model and provider change management as the market shifts
• Prompt and version management with rollback
• Evaluation drift detection so quality does not silently decay
• Cost monitoring and anomaly response
• Incident response for AI-specific failure modes
• Periodic security reviews of prompts, data flows, and access

Discuss Managed Ops

Good fit

This tends to work well when you have:

• 25-500 employees

  Big enough to have real operational workflows, small enough that a focused build moves the needle.

• Repetitive operational workflows

  Processes with volume, variation, and human judgment where AI adds leverage.

• SaaS and data sprawl

  Tools and data that are messy but present — enough raw material to build on.

• No dedicated AI platform team

  You want senior embedded capacity without hiring a full platform org.

• A clear process owner

  Someone empowered to say yes, unblock data access, and champion the change internally.

Not a good fit

We will tell you if it is not, including:

• "Just a chatbot"

  If the goal is a chatbot widget bolted onto a website, that is a different project and a different vendor.

• No data or process owner

  Without someone who owns the workflow and its data, the build cannot land in production.

• No willingness to change the workflow

  AI does not fix a process nobody is willing to adjust. We are not here to automate dysfunction in place.

• AI theater

  If the goal is to look like you are doing AI rather than ship a working system, we are the wrong partner.

• Replace-a-whole-department fantasies

  We augment people. We do not promise to eliminate teams, and we will not take that work.

• Unpaid discovery dressed as a pilot

  If you want free consulting to write an AI strategy memo, that is not what we do.

Why this is different

Built to be left behind, not to keep you dependent.

You own the code

In your repository, under your accounts. No proprietary platform, no runtime dependency on us.

Governance is built in

Access, audit, evals, cost, rollback, and human review — part of the system, not a compliance afterthought.

Your team can run it

Runbooks, handoff, and named champions. The goal is capability transfer, not a recurring invoice.

This is what separates Pavise FDE from generic AI strategy decks, chatbot demos, and vendor-first implementation shops.

Tell us about the first workflow to assess.

One conversation is enough to tell whether FDE is the right shape for where you are. We will be direct about it either way.

Contact Pavise