Frequently Asked Questions

We start with a discovery call to understand your needs, challenges, and goals. From there, we develop a proposal outlining scope, timeline, and pricing. Most engagements follow a phased approach: assessment, design, implementation, and handoff. Throughout, we work collaboratively with your team to ensure knowledge transfer and sustainable solutions.

Engagement length varies based on scope. Quick assessments or strategy work may take 2-4 weeks. Dashboard or analytics implementations typically run 2-3 months. AI solutions range from 1-3 months depending on complexity. We can also provide ongoing support on a retainer basis after initial implementation.

Yes, all our work is remote-first. We use video calls, shared documents, and collaboration tools to work effectively with distributed teams. We're experienced in async communication and can adapt to different time zones.

We work across industries including SaaS, e-commerce, professional services, healthcare, education, and non-profits. While industry context matters, most analytics and AI challenges are universal: improving decision-making, automating manual processes, and measuring what matters.

We typically work on fixed-price projects for defined scopes or monthly retainers for ongoing support. Hourly rates are available for advisory work. All pricing is transparent and agreed upon before work begins. We focus on delivering clear ROI that justifies the investment.

Yes, we routinely sign NDAs and handle sensitive business data. We follow data security best practices including encrypted communications, secure file sharing, and limited data access. We can also work with anonymized or sample data for initial phases.

Absolutely. We often work alongside existing teams to provide specialized expertise, handle overflow work, or mentor team members. We can also help establish processes, select tools, or tackle specific technical challenges your team hasn't encountered before.

We provide comprehensive documentation, training, and knowledge transfer so your team can maintain and extend what we build. Many clients opt for ongoing support retainers for questions, updates, or new features. We're available for follow-up consultations even after formal engagements conclude.

For new projects, we typically can start within 1-2 weeks depending on current commitments. Discovery calls can often be scheduled within a few days. If you have an urgent need, let us know and we'll do our best to accommodate.

Scope changes happen. We handle them through formal change requests that outline the impact on timeline and budget. For retainer arrangements, we prioritize work with you each month. Our goal is flexibility while maintaining clear expectations on both sides.

We're tool-agnostic and select based on your needs. Common tools include Looker Studio, Tableau, Power BI, Metabase for visualization; BigQuery, Snowflake, PostgreSQL for data warehousing; dbt for transformation; and Fivetran, Airbyte for data integration. We can also work with your existing stack.

Not necessarily. Many excellent analytics solutions are affordable or even free (like Looker Studio). We help you choose tools that fit your budget and needs. For most SMBs, mid-tier tools ($500-2,000/month total) are sufficient. We focus on ROI, not selling expensive software.

We build data pipelines that extract data from your various systems (CRM, accounting, marketing tools, etc.), load it into a central data warehouse, and transform it into useful models. This creates a 'single source of truth' where all your data lives together and can be analyzed holistically.

Yes. Data quality is often the biggest challenge. We assess your data issues, implement validation rules, build cleaning pipelines, and establish data governance processes. While we can't fix bad data at the source, we can make sure you have clean, reliable data for decision-making going forward.

That's okay. We work with what you have and help you start collecting the right data going forward. Even a few months of data can provide valuable insights. We also help you implement tracking and measurement so you build a solid data foundation for the future.

Adoption is critical. We involve end users throughout the design process, focus on answering real business questions, keep dashboards simple and actionable, provide training, and embed dashboards into existing workflows (meetings, email reports, etc.). We measure success by usage, not just delivery.

In most cases, yes. We can work with your current tools if they meet your needs. If they don't, we'll recommend alternatives and help with migration. We'd rather extend what you have than start from scratch unless there's a compelling reason to switch.

Yes. Knowledge transfer is built into every engagement. We provide documentation, live training sessions, recorded walkthroughs, and office hours. Our goal is to make your team self-sufficient, not dependent on us for every change or question.

Analytics focuses on understanding what happened and why, using historical data to drive business decisions. Data science includes predictive modeling, machine learning, and forecasting future outcomes. Most businesses need strong analytics before investing in data science. We help you determine which is right for your stage.

We help you build the business case by quantifying current pain points (hours spent on manual reporting, cost of bad decisions, missed opportunities) and projecting ROI from improved analytics. Most analytics investments pay for themselves within 6-18 months through time savings and better decision-making.

No. Modern AI solutions using APIs (like Claude, GPT-4, etc.) don't require ML expertise. We build AI applications using these tools, handle the technical implementation, and train your team to maintain them. You don't need to hire data scientists for most practical AI use cases.

Implementation projects typically range from $15,000-50,000 depending on complexity. Ongoing API costs are usually $200-2,000/month depending on usage volume. Most implementations pay for themselves within 6-12 months through time savings and efficiency gains. We help you calculate expected ROI before starting.

Yes. Enterprise AI providers (Anthropic, OpenAI) have strong security practices and don't train on your data by default. We implement additional safeguards like data anonymization, access controls, and audit logs. For highly sensitive data, we can use on-premise models or Azure OpenAI which offers additional compliance guarantees.

No. We focus on AI augmentation, not replacement. AI handles repetitive, time-consuming tasks so your team can focus on higher-value work requiring human judgment, creativity, and relationship skills. In practice, AI makes your team more productive and their work more interesting.

ChatGPT is a general-purpose tool. Custom AI solutions are built for your specific workflows, grounded in your data and documents, integrated with your systems, and designed for your team's needs. They're more accurate, more secure, and more useful for business operations than general chatbots.

Initial pilots can show results in 4-8 weeks. Full implementations typically take 2-3 months. ROI often appears quickly—if AI saves 20 hours/month of manual work, that's immediate value. We structure engagements to deliver incremental value throughout, not just at the end.

AI isn't perfect. We design systems with human-in-the-loop review for critical decisions, validation checks, and feedback mechanisms to improve over time. We're transparent about accuracy rates and help you determine where AI is appropriate versus where human review is required.

Usually yes. We integrate AI with common tools via APIs, webhooks, or automation platforms like Zapier. If your system has an API or can export data, we can likely connect AI to it. We assess technical feasibility during discovery before committing to implementation.

We primarily use existing AI APIs (Claude, GPT-4, specialized APIs) which are powerful, reliable, and cost-effective. Custom models only make sense if you have unique data/requirements and high volume to justify the investment. For most SMBs, API-based solutions deliver better ROI.

We build solutions that aren't locked into a single provider. Switching between AI providers (Claude, GPT-4, etc.) is typically straightforward since they use similar interfaces. We also help you estimate long-term costs and include fallback options in the architecture design.

We work with modern web technologies (React, Next.js, Python, Node.js) and are comfortable in cloud environments (AWS, GCP, Azure). For analytics, we use SQL extensively and tools like dbt, Airflow, and various BI platforms. For AI, we work with major API providers and orchestration frameworks. We adapt to your existing stack when possible.

Yes. All code follows best practices: version control (Git), testing, documentation, error handling, security reviews, and performance optimization. We deliver code that your team can maintain and extend, not prototypes that break in production.

Absolutely. We collaborate with your engineers through code reviews, pair programming, architecture discussions, and knowledge sharing. We can lead implementation or provide technical guidance to your team depending on your needs.

We build maintainable solutions with documentation, monitoring, and alerts. Post-launch, you can handle maintenance in-house or engage us on a support retainer. We're available for bug fixes, updates, and enhancements as needed. We don't build black boxes—we transfer knowledge.

Yes. Every project includes comprehensive documentation: architecture diagrams, setup guides, API documentation, runbooks for common issues, and inline code comments. We also provide video walkthroughs for complex systems.

We write automated tests (unit, integration, end-to-end) for all custom code, perform manual QA before launch, and run parallel validation when replacing existing systems. We catch issues before they reach production and establish monitoring to catch any post-launch problems quickly.

Yes. We design cloud architectures, set up environments (dev, staging, production), implement CI/CD pipelines, configure monitoring and alerts, and establish security best practices. We can work in your cloud account or help you get started if you don't have one yet.

We follow security best practices including encryption at rest and in transit, principle of least privilege, audit logging, and secure credential management. We can work within GDPR, HIPAA, SOC2, and other compliance frameworks. We discuss security requirements during discovery and implement appropriate controls.