What to Expect in Your First 90 Days With DataTide

Most companies that engage an AI partner have the same fear: “We are going to spend six figures and end up with a strategy deck and no working software.”

That fear is well-founded. The AI consulting industry is full of firms that deliver beautiful presentations and disappear before anything reaches production. We have seen the aftermath companies that paid $200,000 for a roadmap nobody executed, because the firm that built it could not (or would not) write the code.

DataTide works differently. When you engage with us, AI is in production within 90 days. Not a demo. Not a prototype. A production system processing real data, integrated into your workflows, delivering measurable value.

Here is exactly how those 90 days unfold, week by week.

Weeks 1–2: The Strategy Sprint

The first two weeks are the most important. This is where we separate the signal from the noise and make sure we are building the right thing before we write a single line of code.

Day 1–2: Kickoff and Alignment

We start with a half-day kickoff session with your leadership team. This is not a generic “getting to know you” meeting. We come prepared with research on your industry, your competitors’ AI adoption, and the most common high-value use cases in your space.

By the end of the kickoff, we align on:

• Business goals and what success looks like in concrete terms
• Known pain points and processes that are candidates for AI
• Who the key stakeholders are and how decisions get made
• Any constraints budget, timeline, compliance, technical limitations

Day 3–7: Stakeholder Interviews and Process Mapping

We interview 8–15 people across your organization from C-suite to frontline workers. We want to understand:

• Where do people spend time on work that feels repetitive or manual?
• Where do decisions get delayed because information is hard to find?
• What data exists, where does it live, and who can access it?
• What has been tried before, and why did it work or fail?

These interviews surface the real problems not the ones that sound good in a boardroom, but the ones that are actually costing you money and time every day.

Day 5–10: Data Audit

While interviews are happening, our engineering team conducts a thorough data audit. We assess:

• Data accessibility: Can we actually get to the data needed for the top use cases? What APIs, databases, and systems are involved?
• Data quality: How clean is the data? What are the completeness rates, consistency issues, and known gaps?
• Data volume and velocity: How much data is there, and how fast does it change?
• Security and compliance posture: What are the regulatory requirements? Where is sensitive data, and how is it currently protected?

The data audit often reveals surprises both positive (data assets nobody knew existed) and negative (critical data locked in systems with no API access). Better to know now than in week eight.

Day 8–12: Use Case Scoring and Prioritization

We take every candidate use case and score it across four dimensions:

1. Business impact: How much value does this create if it works? (Revenue generated, cost reduced, time saved)
2. Feasibility: How likely is it to succeed given the available data and current technology?
3. Time to value: How quickly can we get this to production?
4. Strategic alignment: Does this advance your broader business strategy?

Each use case gets a composite score. We present the ranked list to your leadership team with our recommendation for where to start. We are blunt about what is and is not feasible. If a pet project scores low, we will say so and explain why.

Day 12–14: Roadmap Delivery

The Strategy Sprint culminates in a concrete deliverable: a 90-day roadmap that specifies:

• The primary use case we will build first, with a clear definition of done
• The data sources, integrations, and infrastructure required
• The team structure who from DataTide, who from your team, and how we work together
• Weekly milestones with measurable checkpoints
• Risk factors and mitigation plans
• Success metrics we will measure at the end of 90 days

This is not a 50-page document. It is a focused, actionable plan that fits on a few pages because the best plans are the ones people actually read and follow.

Weeks 3–4: Solution Architecture

With the roadmap approved, we move into technical design. This is where we make the decisions that will determine whether the system works at scale or falls apart under real-world conditions.

System Design

Our engineers design the end-to-end architecture, including:

• Data pipelines: How data flows from your source systems into the AI system and back. We optimize for reliability and latency, not theoretical elegance.
• Model architecture: Which models (LLMs, embeddings, custom) to use and how they interact. We default to the simplest approach that solves the problem.
• Integration points: How the AI system connects to your existing workflows APIs, webhooks, database triggers, UI components.
• Evaluation framework: How we will measure whether the system is performing correctly, both during development and in production.

Technology Stack Decisions

We select the specific technologies for each component. Our philosophy is pragmatic: we use proven tools that your team can maintain, not cutting-edge tech that requires PhD-level expertise to operate.

Typical stack components include:

• Cloud infrastructure (AWS, Azure, or GCP we work with whatever you use)
• LLM providers with appropriate data privacy guarantees
• Vector databases for RAG use cases
• Orchestration frameworks for complex AI workflows
• Monitoring and observability tools

Security Architecture

Security is designed in from day one, not bolted on at the end. We define:

• Data encryption at rest and in transit
• Authentication and authorization for all AI endpoints
• Data retention and deletion policies
• Audit logging for all AI decisions
• Compliance controls specific to your regulatory environment

By the end of week four, we have a technical blueprint that your engineering team has reviewed and approved. No surprises downstream.

Weeks 5–10: Build and Deploy

This is where the code gets written and the system comes to life. Six weeks of focused execution, delivered in two-week sprint cycles.

How We Work

Two-week sprints with real demos. Every two weeks, we demonstrate working software to your team. Not slides. Not mockups. Functional code processing real data. Feedback from these demos directly shapes the next sprint.

Pair with your team. Our engineers work alongside yours shared repositories, shared Slack channels, shared standups. This is not outsourced development where code appears from a black box. Your engineers see every decision, every tradeoff, and every line of code. When we leave, they know the system inside and out.

Production-grade from day one. We do not build a prototype and then rebuild it for production. Every line of code written during build is production code with proper error handling, logging, monitoring, testing, and documentation. This is slower in week five but dramatically faster in week ten, because there is no “productionize the prototype” phase.

Sprint Cadence

Sprint 1 (Weeks 5–6): Core data pipeline and basic AI functionality. By the end of this sprint, data is flowing through the system and the AI component is producing outputs even if they are not yet polished.

Sprint 2 (Weeks 7–8): Refinement, edge case handling, and integration. The system connects to your real workflows. Edge cases from real data are identified and handled. Evaluation metrics are tracked.

Sprint 3 (Weeks 9–10): Production hardening, performance optimization, and launch preparation. Load testing. Security review. Runbook creation. User acceptance testing with your team.

Evaluation Frameworks

One of the most important things we build is the evaluation framework the system that tells you whether the AI is working correctly.

For every AI system, we define:

• Accuracy metrics: What percentage of outputs are correct, and how do we measure it?
• Failure modes: What does the system do when it is not confident? How does it escalate to humans?
• Performance baselines: What was the human performance before AI, and what is the AI performance now?
• Drift detection: How do we know when the system’s performance is degrading over time?

This framework is not optional. It is how you maintain trust in the system after launch, and it is how you justify continued investment.

Weeks 11–12: Optimize and Handoff

The system is in production. Real users are interacting with it. Now we tune, train, and ensure your team is set up for long-term success.

Performance Tuning

With real production data flowing, we optimize:

• Prompt tuning based on actual user inputs and edge cases
• Latency optimization to ensure response times meet user expectations
• Cost optimization to reduce unnecessary API calls and compute spend
• Accuracy improvements based on evaluation framework data

Team Training

We conduct hands-on training sessions with your team covering:

• System architecture and how all components interact
• How to modify prompts and update system behavior without engineering changes
• How to read monitoring dashboards and respond to alerts
• How to diagnose and fix common issues
• How to extend the system for adjacent use cases

This is not a “watch us present” training. It is a “you do it while we watch and coach” training. By the end, your team has independently made changes to the live system under our supervision.

Measurement and ROI Documentation

We measure the system against the success metrics defined in week two:

• How much time or money has the system saved?
• What is the accuracy compared to the human baseline?
• What is the user adoption rate?
• What is the system reliability and uptime?

These numbers go into a concise report that you can share with leadership concrete evidence of ROI, not vague promises.

Next Steps Planning

Based on what we have learned in 90 days, we map out the next phase:

• Which additional use cases are now feasible (because the foundational infrastructure exists)?
• What optimizations would increase the value of the current system?
• Where are the remaining data gaps that limit future AI capabilities?
• What internal capabilities should you build to reduce external dependency?

Three Paths After 90 Days

At the end of our initial engagement, companies typically choose one of three paths:

Path 1: Expand. The first 90 days delivered clear ROI, and there are more high-value use cases to pursue. We continue building, typically at a reduced engagement level because the foundational infrastructure already exists. Each subsequent project is faster and cheaper than the first.

Path 2: Support. The system is in production and your team can handle day-to-day operations, but you want DataTide available for optimization, troubleshooting, and strategic guidance. We shift to a lightweight support engagement a few days per month to keep things running smoothly and advise on next steps.

Path 3: Fly solo. Your team is fully capable of maintaining and extending the system independently. We do a clean handoff, ensure all documentation is complete, and step back. No recurring fees, no vendor lock-in. You own everything.

All three paths are good outcomes. The right one depends on your internal capabilities, your AI ambitions, and your budget.

Our Commitments to You

Transparency. You will never be surprised by what we are building, how much it costs, or whether it is working. Our process is designed for radical visibility weekly updates, shared dashboards, open access to all code and documentation.

Honesty. If something is not working, we will tell you first. If a use case is not feasible, we will say so before you invest in it. If the best answer is a $100/month SaaS tool instead of a custom build, we will recommend it even though it means less revenue for us.

Ownership transfer. We build to leave. Every system we create is designed to be maintained by your team. Every decision is documented. Every engineer on your team who works with us leaves the engagement more capable than when they started.

Results within 90 days. Not a plan for results. Not a prototype that might become results. Actual, measurable, production-grade results within 90 days of kickoff.

Ready to see what 90 days can look like for your organization? Book a free introductory call. We will discuss your goals, your data, and your team and give you an honest assessment of what is achievable. If DataTide is the right fit, you will know exactly what to expect from day one. If we are not the right fit, we will tell you that too.