Build AI Solutions That Actually Work

• AI now leads Rootstrap's new business growth
• We prototype-to-spec custom AI solutions in 3-5 weeks: v1 launches in 10-14 weeks.
• Our AI tooling cuts build time and cost by 20-30%.

Most teams want AI. Few know where to start.

Enterprise adoption of AI doubled during the first half of this year (5% to 10%), yet most firms still don’t know how to get started. ChatGPT now has 800 million users, up from 400m just over the past few months, making many individual workflows faster, but robust AI systems demand more than clever prompts and common data sources. That’s where Rootstrap comes in.

AI now anchors the majority of our new client builds. From B2B automation to consumer copilots, we help define, design, and deliver AI solutions that create real ROI. Here’s how:

1. Structured Data & Enrichment

Every strong AI product starts with the right data infrastructure. You need to:

• Ingest & centralize diverse data sources (first-party, third-party, public and private)
• Clean, structure, label & tag to enhance LLM context
• Decide between traditional vs. vector databases or both (RAG trade-offs matter)
• Build continuous pipelines & feedback loops (even basic reinforcement learning helps improve outcomes over time)

Our crack data engineering, data science and AI team helps tackle all of this by defining the right architecture during the initial discovery process then implements with rigorous QA to ensure consistent accuracy.

2. Agentic Integrations

Agents are all the rage these days, and it's no surprise why. An agent is simply an LLM that can call APIs (or MCP servers) to 'read' and/or 'write' data across your stack. 

• A popular 'read' use case is incorporating 'Deep Research' into an AI solution, essentially the ability to run a web search in order to synthesize and incorporate the results into the AI's context at the time of the prompt alongside the rest of your more evergreen data.
  
• A popular 'write' use case is adding/updating records to a CRM, ERP or similar; booking a meeting or placing a purchase order; and creating draft content within social media platforms -- all from within a single custom AI solution. 

We define these agentic requirements during our discovery process and, if necessary, build custom APIs or MCPs with our senior backend and AI engineering teams.

3. AI-First UI & Access Controls

Finally, it wouldn't be a full-service solution without Product & Design. How will users actually interact with the AI? Whether it's a consumer-facing app or a B2B SaaS tool, we design intuitive, easy-to-use AI-first user interfaces via custom web apps and mobile apps, and we can also integrate these solutions into common platforms like Webflow, Wordpress, Shopify and more.

Best practices in AI-UI are still emerging, but our talented Product and Design team stays ahead of the curve through continuous, extensive market research and user testing for our clients. 

From a consumer-facing perspective, pre-prompt interactive buttons, voice, 'memory' management, and managing latency cues are among the most important ways to enhance engagement and steer clear of tedious 'chatbot' territory. Brand safety is also critical: how do you prevent your AI not just from hallucinating about your brand or product but saying abusive/offensive things to your customers (typically in response to a troll trying to provoke it).

For B2B SaaS, it's more about 'seeing what the agent is doing', being able to audit/edit the results (often within a traditional UI for familiarity and expedience over 're-prompting'), data security, and user-based access controls. For example, perhaps the finance department should be able to retrieve everyone's salary via their AI solution for review, but other departments should not. 

Our Product team defines all of these considerations and requirements during our discovery process, and our Design team rapidly iterates and tests prototypes with users to drive to an optimal solution. 

We Use AI to Build AI

Of course, to lean into AI, you have to lean all the way in. That's why we leverage AI tools ourselves extensively throughout the product development process. In Product & Design, we use tools like Replit, Loveable, UIPilot and Figma's AI features to create working prototypes much faster, meaning we can accelerate and increase our total number of test cycles with users. In Engineering, we use Cursor and a few QA tools to accelerate coding and testing by up to 20-30%, which we pass onto partners in the form of faster timelines and lower project costs.

Zero to One, Faster Than Ever

So yes, there's a lot to consider when building AI solutions, much of which is new and fast emerging. Thankfully, our team is expert at rapidly defining problem/solution spaces with this AI framework, so typically we get partners from 0 to clear product definition, working prototype and detailed technical roadmap in as few as 3-5 weeks, and a production-ready v1 in 10-14 weeks total.

If you're interested in exploring what AI can do for your business, but need a partner who can guide you through the process quickly and affordably, get in touch for a complementary consultation and quote for your needs.

Start your AI acceleration: Book a 30-min call to scope your solution with our expert team.