Fresh Water Futures
GenAI Prototypes and MVPs - chat, sales, support and more. Specialists in trusted AI in privacy and security sensitive applications.
freshwaterfutures.comWhat They're Known For
I’ve either worked with Fresh Water Futures directly or seen their work close-up through people who have, and what they’re actually known for is turning messy enterprise data and privacy constraints into useful GenAI prototypes quickly. They’re not trying to be a full-stack agency that does everything; instead they focus on chat and assistant-style MVPs — sales copilots, internal support bots, and customer-facing chat where trust and data sensitivity matter. Their real strength is the combination of practical RAG-style pipelines (embeddings + vector search), Google Drive/Sheets connectors, and a disciplined approach to data handling — they can pull a dozen different spreadsheets and docs into a searchable knowledge base, map and clean the data, then wrap a guarded LLM on top so the answers are auditable and less likely to expose PII.
Best For
If you need a prototype or an MVP where the data is the hard part, they’re a great match. Examples where they shine: companies with lots of business-critical info living in Sheets/Drive that want a searchable sales assistant, internal tools that need strict logging and access controls, or pilots that must sit behind strong privacy controls for compliance reasons. They’re also good when you want a fast, testable demo that proves value before investing in a larger product — think 4–8 week proof-of-concept work where the goal is: “Can this model reliably surface the right company policy, contract clause, or lead info?” rather than “ship a polished consumer app.”
How They Work
Their process is pretty methodical and client-friendly. Expect an intensive discovery focused on data sources and access patterns, a short engineering sprint to build connectors and a vector store, then iterative demos where they refine prompts, answer attribution, and guardrails. Communication tends to be pragmatic: regular demo sessions, shared documentation (they like to keep provenance and audit trails visible), and a lot of asynchronous updates. They’re deliberate about adding safety layers — redaction rules, role-based access, and human-in-the-loop approval workflows — so if your organization needs to demonstrate controls for audits, their documentation and deployment patterns make that easier.
About Fresh Water Futures
Fresh Water Futures is an innovative suite of tools designed to navigate the complex landscape of AI-driven solutions, specializing in GenAI Prototypes and MVPs. With a focus on enhancing various facets of communication, including chat, sales, support, and beyond, Fresh Water Futures stands as a beacon of excellence in the realm of AI technology. At its core, Fresh Water Futures prides itself on the integration of cutting-edge AI capabilities while prioritizing privacy and security in sensitive environments. The tools offered by Fresh Water Futures are meticulously crafted to foster trust and reliability, making them ideal for applications where privacy and security are paramount.
Considerations
There are practical things to consider before hiring them. Pricing sits at the mid-to-premium range because of the extra work around security, governance, and auditability — you’re paying for risk mitigation, not just chat UI. Timelines for a prototype are quick, but moving to production and getting sign-off from security/compliance can add weeks. Also, their safety-first approach is a tradeoff: you’ll get reliable, conservative outputs, which is great for compliance but sometimes less creative than a loosely constrained model. Expect ongoing costs for hosting secure vector stores, monitoring, and model usage; it’s not a one-off, cheap widget. Finally, because they focus on privacy-sensitive applications, they may push back on exposure to raw production data until proper controls are in place, which can slow down data-heavy projects.
When to Look Elsewhere
There are times you should look elsewhere. If you’re a consumer app that needs highly polished front-end UX, large-scale user acquisition, or ultra-low latency at huge volumes, they’re not the best fit — they’re more about trustworthy AI than shaving milliseconds off inference time for millions of users. If your priority is the absolute lowest cost prototype, or you need a native mobile or embedded solution with specialized hardware inference, pick a different shop. Also, if you need domain expertise in an extremely narrow regulated field (like clinical trials or FDA-regulated medical devices) you should vet them closely — they can handle privacy patterns, but the agency-level regulatory experience for very specialized industries isn’t their default claim.
