FAQ
Straight answers before you commit.
What enterprise teams ask us about proof, ownership, models, security, and delivery.
The execution gap
Five questions keep a pilot a pilot. Each one is answered below.
Which opportunities are worth pursuing?
Answered inProof of ImpactWhat will create measurable business value?
Answered inProof of ImpactHow will it work with existing data, technology, and processes?
How will it be evaluated, governed, and operated?
Answered inSecurity and governanceWho is responsible for getting it into production?
AI Xccelerate answers those questions through execution. The work produces direction and a working system, not another strategy presentation.
Proof of Impact
Proof before the big commitment.
The first engagement delivers an AI roadmap and a working Proof of Impact. You make the production decision on evidence from your own environment.
What is a Proof of Impact?
One prioritized opportunity, built as a working system in your environment, against a success definition agreed before we start. It runs alongside discovery, so the roadmap and the first working system get built at the same time. No six-month assessment before anything ships.
How do you choose the use case?
We compare candidate opportunities and select one that is meaningful, measurable, and feasible to test. We weigh business value, workflow clarity, feasibility, measurability, sponsorship and adoption, and whether the proof will answer questions that matter for production.
What do we have at the end?
A working system in your cloud, under your identity and governance. An evaluation harness measuring it against the agreed definition. Evaluation findings, including limitations and risks. And an honest assessment of what production would take, with a recommendation for the next decision.
Is the Proof of Impact production-ready?
No. A Proof of Impact is not production. It is a working system built to an agreed success definition so you can decide with evidence. Production requires additional engineering, integration, security review, governance, testing, adoption work, and operational preparation, and we tell you what that will take.
What happens after the proof?
The engagement ends at a gate with four options: proceed, refine, pause, or stop. Nothing advances on schedule alone. Stopping a weak use case early counts as a good outcome.
What if the evidence is negative?
That can still be valuable. Learning that an opportunity should stop or change before production can prevent a larger misallocation of time and investment.
Do we need a fully defined use case before we start?
Not necessarily. Prioritization is part of the engagement, but a viable proof still requires meaningful priorities, accountable sponsorship, operating context, and enough access to define a focused scope.
Ownership and your cloud
Built in your cloud. Owned by your teams.
Systems run inside your own environment: your data, identity, and infrastructure. They are handed to your teams to own and operate.
- Your identity
- Your data
- Your governance
Who owns what you build?
You do. Systems are built inside your cloud, under your identity and governance, and handed to your teams to own and operate.
Where does the system run?
In your environment. That means your cloud (Microsoft Azure, AWS, Google Cloud, Cloudflare, Vercel, or Railway), your data architecture, your identity and security controls, and your governance requirements.
What about AI Xccelerate's own tools?
Our infrastructure is an accelerator, not a silo. We bring components such as Parchment, Agent Mem, Agent DB, and AIX Core when they make delivery faster, and you are never forced to adopt them. The agreement separates customer-specific work from reusable AI Xccelerate foundations and sets the reuse rights for each.
Will our team be able to run it after handover?
That is the goal: clear accountability after handover. The operating, handoff, enablement, support, and ownership model is defined before production, and Forward Deployment includes capability transfer to your internal teams. The exact model depends on the agreement and your internal capability.
Do you resell software or platforms?
No. We are platform-, model-, framework-, and infrastructure-agnostic, and we do not resell, so the recommendation reflects what fits your organization.
Model choice
Your models, your choice. We adapt.
We fit your technology strategy and stay opinionated about how agentic systems get engineered. The model sits inside that design as a choice, not a lock-in.
- OpenAI
- Anthropic
- Google Gemini
- Microsoft Copilot
- Open source
Which AI models do you work with?
OpenAI, Anthropic, Google Gemini, Microsoft Copilot, and open-source models. The model is your choice. AI Xccelerate is an Anthropic partner and an OpenAI partner, and a Microsoft Cloud AI partner.
Why does being model-agnostic matter?
Because the recommendation reflects your needs, not a reseller agreement. Many teams already use Copilot, ChatGPT, and Claude day to day; a model-agnostic build lets production systems use the providers your enterprise already approved. The model is chosen against your evaluation criteria, and the system is not tied to one vendor's roadmap.
Can we switch models later?
Yes, as a governed change. Models, instructions, knowledge, tools, and permissions are treated as production changes, and a change triggers review and re-evaluation before the system keeps working on it.
Does the engagement require the AI Xccelerate platform?
No platform-first decision is required. Existing workers, products, or reusable platform capabilities may be used where they accelerate the appropriate implementation.
Can you integrate with any system?
We don't claim universal connectors. APIs, authentication, scopes, objects, actions, rates, error handling, production status, and commercial scope are verified for each required integration.
Security and governance
Governance runs through every phase.
Governance and evaluation are not a phase. They run from the first working session to production, so trust comes from clear answers, inspectable evidence, and honest limitations.
How is an AI system governed?
Each system gets a defined role, scoped authority, and operating evidence, with human oversight, evaluation, and ownership at every step. We map what it reads, writes, and changes, and every connection needs a business purpose and scoped authority.
Where does our data go?
The answer depends on the system architecture, deployment model, integrations, model providers, and support process. The relevant review should include a current data-flow diagram and contractual documentation.
Can AI workers act without human approval?
Only within the role, permissions, and human-control model approved for the use case. Consequential or uncertain actions may require review, approval, or escalation. Authority should expand only when evidence supports it.
How is the system tested?
Success criteria are defined before testing. Tests cover representative, difficult, ambiguous, and sensitive scenarios, plus failure, recovery, and prohibited-action tests. In production, evaluation continues, and changes, exceptions, and incidents trigger review and re-evaluation.
Who is responsible for operating the system?
Operation may be customer-led, shared, or supported by AI Xccelerate depending on the product and agreement. Responsibilities, service levels, incident handling, and improvement ownership are documented in a shared-responsibility matrix and the applicable agreement.
Is AI Xccelerate SOC 2 compliant?
Certification status must be confirmed from current documentary evidence for the relevant product or service. We do not use intention or preparation as a substitute for a completed, in-scope certification.
Forward-deployed engineering
Engineers in your workflow, not a deck on your desk.
AI practice leadership and forward-deployed engineering work as one team, accountable to one business outcome until the system runs in production.
How is this different from a traditional consultancy?
A strategy engagement produces a plan. A forward-deployed team also builds, integrates, and evaluates the working system inside your environment, then recommends whether to stop, refine, deploy, or expand based on that evidence. The work produces direction and a working system, not another strategy presentation.
What does a forward-deployed AI engineer do?
A forward-deployed AI engineer works inside your operating context to understand the real workflow and system boundaries, design AI responsibilities and human controls, build, integrate, and evaluate the working system, and support its move into production.
Is Forward Deployment staff augmentation?
No. Staff augmentation supplies capacity that the customer directs. Forward Deployment connects opportunity definition, system design, implementation evidence, and production decisions within an agreed outcome and bounded scope.
How is it different from a software vendor's engineers?
A software vendor's forward-deployed engineers deploy that vendor's product. AI Xccelerate is platform-, model-, framework-, and infrastructure-agnostic and does not resell, so the system fits your organization.
Who is on the team?
The model combines AI practice leadership and forward-deployed engineering. Titles, team composition, allocation, and duration are defined for each engagement.
What do you need from our side?
An accountable executive sponsor and process owner, representative users and subject-matter experts, and technology, data, architecture, and governance participation. Plus timely access and decisions, agreement on success measures, and a defined owner for operation after deployment.
Getting started
Start where it makes sense.
Each engagement has a defined scope and a defined end, produces something useful by itself, and sets up the next.
See the six engagementsWhere do most organizations start?
Most start with Discover and Prove: an AI roadmap and a working Proof of Impact, built in parallel. Others start with Fractional AI Leadership when they need senior AI judgment in the room before committing to a project.
How long does an engagement take, and what does it cost?
Both depend on scope, and we will not quote either before we understand the work. Duration and investment are set together during scoping, against the outcome you need and the constraints you actually have.
What does the first conversation cover?
Your business and execution context. It is not a platform demonstration. The question we start with is what your organization needs AI to materially change in the next 12 months.
Still have a question?
What does your organization need AI to materially change in the next 12 months?
Bring the question this page didn't answer. The first conversation focuses on your business and execution context, not a platform demo.