Acceleration with Build Together

Acceleration with Build Together#

In the program Build Together, the same team that develops and operates the 19 products of VertexHub – using stack Rust, Go, Svelte, PostgreSQL, Kubernetes and integrated AI from the first commit – starts working on the client project. We apply the same engineering discipline, quality standards and reusable components to deliver market‑ready MVPs quickly and predictably.

How speed becomes part of the process#

• Alignment – We run discovery workshops to understand the client’s real challenge and define success criteria.
• Architecture – We produce an architecture document that describes the stack, the AI components (RAG, LLM agents) and delivery milestones. At this stage the scope and investment are defined, without a fixed price table.
• Co‑construction – We deliver testable increments in short cycles, with code review, staging environment and CI/CD automatically configured. Each cycle ends with a functional demo for the client.

The infrastructure is already ready (k3s clusters, Kata Containers, Envoy Gateway), which eliminates the need for manual provisioning and allows the team to focus directly on business logic.

Case 1 – Medical triage platform (health startup)#

Challenge – A telemedicine startup needed an automatic triage module that collected symptoms via chat, classified urgency and routed the patient to the appropriate specialist.

Build Together application

1. Alignment – We identified the critical triage flows, summary record generation and scheduling.
2. Architecture – We defined Go (Echo) for the API, SvelteKit for the UI and an LLM agent connected to a vector DB for clinical protocol search.
3. Co‑construction – We reused the messaging layer of Vertex ChatSense to integrate chat with WhatsApp, implemented the risk‑classification service using RAG and created the scheduling API.

The MVP was delivered quickly, allowing the startup to launch a pilot and gather enough feedback to validate the business model.

Case 2 – Credit analysis solution (fintech)#

Challenge – A fintech needed a risk‑assessment service that combined structured and unstructured data to generate real‑time decisions.

Build Together application

1. Alignment – We mapped decision points and integration requirements.
2. Architecture – We chose Rust (Axum) for the high‑performance layer, PostgreSQL as the primary store and an LLM agent to interpret free‑text. Service orchestration follows the pattern already used in our products.
3. Co‑construction – We delivered the data ingestion API, the scoring micro‑service that blends a statistical model with insights generated by the LLM agent, and a SvelteKit dashboard for visualization and audit.

With the MVP in hand, the fintech integrated the solution into the onboarding flow, obtaining the validation needed to move to production.

Case 3 – Intelligent tutoring platform (edtech)#

Challenge – An edtech sought a virtual tutor that tracked student progress, suggested content and cited sources obligatorily, complying with LGPD and CDC requirements.

Build Together application

1. Alignment – We defined the minimum functionalities for class recommendation, question generation with source references, and dropout‑risk monitoring.
2. Architecture – We used Go for the recommendation API, Rust for the response‑generation engine with RAG, and a vector DB for embedding storage. The front‑end was built with Svelte 5, leveraging reusable components from our pre‑launch EduTechMaster.
3. Co‑construction – We delivered the recommendation module, the LLM tutor that inserts automatic citations, and the dropout‑prediction engine that proactively triggers the tutor.

The MVP enabled the edtech to start offering personalized tutoring and collect data to improve the student experience.

Costs under control thanks to shared architecture#

• Budget defined before development – The Architecture stage generates a scope document that serves as an internal contract, avoiding unexpected changes.
• Component reuse – AI libraries, CI/CD pipelines and Kubernetes infrastructures are already consolidated in our products, reducing implementation effort.
• Milestone transparency – Each co‑construction cycle delivers a testable increment, allowing priority adjustments without additional rework costs.

Concrete results that go beyond the MVP#

• Market validation – In all cases, the MVP was sufficient to start testing with real users, collect usage metrics and confirm business hypotheses.
• Scale foundation – The architecture incorporates observability best practices, security (LGPD) and native AI, facilitating the transition from MVP to full production.
• Rapid return – The short delivery timeline lets the startup begin generating revenue or reducing operational costs before completing the full development phase.

Next steps#

If your company has a problem that can be solved with AI – whether intelligent assistance, risk analysis or personalized tutoring – the Build Together model offers speed, predictable cost and the expertise of a team that already delivers production products.