Most businesses considering AI know they want to implement it — but have no clear picture of what that actually involves. What happens after you book the first call? What does the consulting firm actually do? What will you be asked to provide? What gets built, and when?
This article walks through a typical CyberCore AI consulting engagement from first contact to production deployment — the stages, what happens at each one, and what you should expect from a consulting-led AI firm.
Before the engagement: what to prepare
You don't need a technical specification, a detailed brief, or clarity on which AI approach is right before the first call. What you do need is a clear picture of the business problem:
- What is the process or workflow that you want AI to improve?
- What does it currently involve? Who does it, how long does it take, what does it cost?
- What would success look like in six months?
- What data do you have that's relevant to this problem?
The AI consultant's job is to translate a business problem into the right technical solution. Your job is to describe the business problem clearly.
Stage 1: Discovery Consultation
Discovery Consultation — 30–45 minutes
A structured diagnostic conversation. The consultant asks questions about your business problem, current process, data environment, compliance requirements, and definition of success. No pitch, no product demo.
The discovery consultation is not a sales call. A consulting-led firm uses this time to understand your problem — not to sell you a solution. You should expect to be asked:
- What specific business problem are you trying to solve?
- Who currently handles this process, and how?
- What data is involved — documents, structured data, APIs?
- What systems does the solution need to integrate with?
- What compliance or data handling requirements apply?
- What does a successful outcome look like — and how will you measure it?
At the end of a good discovery consultation, you should feel like you've been heard and understood. The consultant should be able to summarise the problem back to you accurately and — even at this early stage — begin articulating an approach.
Red flag: if the first call ends with a product pitch rather than a problem summary, the firm is a vendor, not a consultant.
Stage 2: Scoping and Proposal
Scoping and Proposal — 5–10 working days
The consulting firm translates the discovery into a written proposal: recommended approach, architecture overview, timeline, deliverables, and confirmed investment. All investment is agreed before work begins.
After the discovery consultation, the AI consulting firm prepares a bespoke proposal. This is not a generic deck — it should be specific to your problem, your data environment, and your constraints.
A good proposal includes:
- A restatement of the business problem and success criteria
- The recommended AI approach (RAG, agents, automation, or a combination) with rationale
- A high-level architecture description — what gets built and how it connects to your existing systems
- Specific deliverables with clear acceptance criteria
- Timeline broken into phases with milestones
- Total investment and billing structure — typically milestone-based
At CyberCore, all engagement investment is confirmed in the proposal before a single line of code is written. The billing structure is 40% at project start, 30% at mid-point milestone, 30% on delivery.
Stage 3: Architecture and Technical Design
Architecture Design — 1–2 weeks
Detailed technical design of the AI system — model selection, architecture pattern, integration design, data pipeline, error handling, and human-in-the-loop touchpoints. A technical design document is produced for client review.
Before any code is written, the architecture is designed and documented. This stage defines the foundational decisions that can't easily be changed later:
- Which AI model(s) will be used, and why (GPT-4o, Claude Sonnet, Gemini, or others depending on requirements)
- Whether the approach is RAG, fine-tuning, agentic, or a combination
- How the AI connects to your existing systems (API integrations, database connectors, authentication)
- How data flows through the system — ingestion, processing, retrieval, generation
- Where human review is built into the workflow
- How the system handles errors, edge cases, and unexpected inputs
You'll review and approve the architecture before build begins. This is where questions about compliance, data handling, and integration requirements are resolved — not mid-build.
Stage 4: Build and Integration
Build and Integration — 3–6 weeks
The AI system is built and integrated with your existing tools. Regular progress check-ins throughout. A test environment is stood up for client review before production.
The build stage follows the approved architecture. You'll receive regular progress updates — not daily noise, but structured check-ins at meaningful milestones.
What you'll typically be asked to provide during build:
- Access credentials for the systems the AI needs to integrate with
- Sample data or documents for testing (often 20–50 representative examples)
- Feedback on test outputs — does this answer look right? Does this response format work for your users?
- Review and sign-off at defined milestones
A test environment is usually stood up mid-build so you can review the system with your actual data before it goes to production.
Stage 5: Testing and Validation
Testing and Validation — 1–2 weeks
The system is tested against real business scenarios. Output quality, accuracy, edge case handling, and error management are validated. Adjustments are made based on test results.
Testing is not just "does it work" — it's "does it work well enough for real business use." This means:
- Testing with real examples from your actual use case, not synthetic test cases
- Measuring accuracy against your success criteria
- Testing edge cases — what happens when the input is unusual, incomplete, or outside the expected range?
- Testing error handling — what happens when an API is down, a document is malformed, or the AI is uncertain?
- Review by the people who will actually use the system
Adjustments identified during testing are addressed before production deployment.
Stage 6: Production Deployment
Production Deployment
The system is deployed to production, with monitoring, alerting, and defined review protocols in place. A handover package covering operation, maintenance, and known limitations is provided.
Production deployment includes:
- Deployment to your production environment (or a managed environment, depending on scope)
- Monitoring configuration — alerts for errors, usage anomalies, or accuracy degradation
- Handover documentation covering how to use the system, how to update it, and known limitations
- A defined support period for post-deployment issues
Total timeline: A focused first AI engagement — from discovery call to production deployment — typically takes 6–10 weeks. More complex engagements with multiple integrations or broader scope take longer. The biggest determinants of timeline are integration complexity and the speed of client feedback cycles.
Ready to start your AI engagement?
Book a discovery consultation with CyberCore. 30–45 minutes, structured questions, no pitch. We'll tell you whether AI is the right solution for your problem — and if so, what the right approach is.
Book a Discovery CallFrequently asked questions
How long does an AI consulting engagement take?
A focused first AI engagement typically takes 6–10 weeks from discovery call to production deployment. Discovery and scoping takes 1–2 weeks. Architecture takes 1–2 weeks. Build and integration takes 3–6 weeks. Testing takes 1–2 weeks. Complexity and integration scope are the primary timeline drivers.
What happens in a discovery consultation for AI?
A structured 30–45 minute conversation covering the business problem, current processes, data environment, compliance requirements, integration constraints, and definition of success. A good discovery call ends with the consultant understanding your problem — not with a product pitch.
What does an AI consulting firm deliver?
A build engagement delivers a production AI system — the AI pipeline, integrations with existing tools, testing documentation, deployment configuration, and handover materials. A consulting-only engagement delivers strategy, technical architecture recommendations, and an implementation roadmap.
How is a CyberCore AI consulting engagement priced?
All engagement investment is confirmed in the proposal before work begins. Engagements are project-based with a 40/30/30 milestone billing structure — 40% at project start, 30% at mid-point milestone, 30% on delivery.