Enterprise software, built faster. Built deeper.
Speed is the easy part. Depth is what survives production. We use AI to accelerate how we build and embed AI into what we build — but what we hand over are real systems: architected, secured, observable and operated at enterprise scale.
AI-accelerated engineering inside a structured methodology. Weeks of work compressed into days — with the same quality gates, not fewer.
Complex domains, real architecture and AI embedded at the core — systems built to be extended for years, not replaced next quarter.
What “deeper” looks like in practice
Speed is the easy part — anyone with an agent can ship quickly. Depth is what you can still prove three years and forty engineers later. Every claim below is one of three things: a check that turns red, a number that cannot move backwards, or a decision you can read.
Tenant isolation: certified, not promised
Multi-tenant isolation isn't an architecture diagram — it's enforced at the database engine and certified against real Postgres, using a synthetic least-privilege role that mirrors production, across roughly 160 dedicated boundary tests.
The proof: when an enterprise buyer or auditor asks “how do you know?”, the answer is a reproducible test run — not a paragraph.
Security coverage that can only move forward
Every isolation guarantee is pinned as a hard number inside a test. Add a table without an isolation policy — or try to widen the exemption list — and the build goes red. Progress is mechanically ratcheted: it cannot be undone quietly.
The proof: security here doesn't erode with deadlines, time, or team rotation — the system refuses to move backwards.
We know exactly how AI-written code fails
AI-generated code rarely fails carelessly — it fails plausibly: it looks correct and breaks at the edge. We maintain a catalog of those failure modes, learned from real bugs, each with a name, an example, and a countermeasure built into review.
The proof: institutional memory about a brand-new discipline — every project starts with the lessons of the last one, not the same learning curve.
AI quality is measured, not perceived
Answer quality runs against a curated, versioned set of golden cases — production samples, edge cases, adversarial inputs, and every quality bug ever closed. Releases are gated by delta against the previous baseline, never by gut feel or absolute scores that drift with the judge model.
The proof: “the system got worse” stops being an impression and becomes a number someone has to explain before shipping.
It says “I don't have enough information” before it invents
Hallucination control is configured per knowledge base in three postures — off, shadow, enforce. Shadow mode measures the exact impact of enforcement without a single additional model call, and if the verification judge itself fails, the system fails closed: it degrades rather than guesses.
The proof: you switch enforcement on with a measured bound of its effect — not a hunch.
Public means public — on every retrieval path
A public surface is restricted to published content on every leg of the search, not just the obvious one. Graph reasoning shuts off entirely for public callers — because traversing relationships between entities can reveal what no single document exposes on its own.
The proof: you can open a portal or a widget to the world without opening a side door.
One point of control across eight AI providers
Direct cloud, your own cloud account, and self-hosted endpoints all sit behind a single gateway. Failover between providers is automatic and fully audited — original provider, replacement, error class, latency — and in strict sovereignty mode, falling back to a provider that isn't yours is deliberately suppressed.
The proof: you change models or providers through configuration — no product rewrite, no vendor lock-in, and sovereignty when the contract demands it.
An honest map of what isn't verified yet
Our technical documentation declares its own blind spots in the same chapter that reports its coverage, and our measurement harness distinguishes “measured and failed” from “couldn't measure with confidence.” No silent gaps, no coverage theater.
The proof: a team that names its gaps is the only team whose declared coverage you can actually believe.
The old model is too slow and too shallow.
Multi-year enterprise builds
Markets and requirements move before launch
Large delivery teams stitched across vendors
Coordination overhead consumes the budget
AI added as a feature after the product is shipped
Surface-level AI that doesn't change how the product works
Quality, security and observability deferred to "later"
Technical debt and rework starting on day one
No repeatable method for embedding AI into the product
Inconsistent results and unpredictable timelines
Most teams have added AI tooling to their workflow. Few have rebuilt their engineering practice around it — and fewer still know how to ship software where AI is part of the core product, not a feature glued on at the end.
Faster is the entry ticket. Deeper is the difference.
Boostack is an AI-native product engineering team working on two axes at once: AI accelerates how we build, and AI is embedded in what we build. Speed alone is commoditized — what we protect is depth: real architecture, complex domains, security and operations that hold up in production for years.
How we build
A multi-stage engineering pipeline — discovery, architecture, build, hardening — with quality gates at every step and senior engineers in the loop. AI handles the repetitive work (boilerplate, tests, docs, refactors) while humans own architecture, security and product decisions. The result: weeks of work compressed into days, without losing the discipline of an enterprise SDLC.
What we build
Production software where AI is a first-class building block, not a bolt-on. We combine deterministic logic (workflows, rules, integrations) with generative capabilities (RAG, agents, copilots, structured extraction) inside the same product — observable, evaluable, and governed. Every AI feature ships with guardrails, evals and human-in-the-loop where it matters.
Where it runs
Google Cloud Platform is our default production environment and the area of our deepest operational expertise: GKE/Cloud Run for compute, Cloud SQL and BigQuery for data, Vertex AI for models, plus IAM, VPC-SC and audit logging for enterprise-grade security. One platform, end-to-end, ready for the most demanding workloads and compliance requirements.
We don't only build for clients. We ship our own product.
Sequentia is Boostack's flagship product — an AI-native Knowledge Operating System, live in production today. It is our proof of depth: a large, multi-tenant enterprise system with workspace isolation, row-level security, envelope encryption and hybrid retrieval, built and operated over time — not a sprint demo.
The knowledge engine behind every customer, employee and AI agent.
AI quality is bounded by knowledge quality. Sequentia gives organizations a governed system of record for knowledge, and serves it — with hybrid retrieval — to help centers, support agents, copilots and autonomous agents through a single consistent layer.
Built, operated and scaled entirely by the Boostack team, on the same methodology and the same Google Cloud production standard we apply to client work.
Governed knowledge base
Versioned articles, taxonomy, RBAC and a lifecycle state machine — one authoritative source of truth per workspace.
Hybrid retrieval engine
BM25 lexical search fused with vector RAG and a knowledge graph, so answers cover both relevance and relationships.
Every AI surface
Branded help centers, customer Q&A bots and an agent-side copilot all read from the same governed layer.
Closed-loop lifecycle
Resolved conversations become drafted, reviewed and measured knowledge — the system improves as it is used.
Go deeper into how Boostack works.
AI-native Knowledge Operating System. The live proof of how Boostack builds, ships and scales enterprise AI.
How we build
The AI-accelerated cycle and the engineering principles behind every build.
What we build
Product engineering, AI systems, and the modern stack we run in production.
Frequently asked questions
Quick answers about how Boostack works, who we partner with, and what we ship.
- What is Boostack?
- Boostack is an AI-native product engineering team that delivers enterprise-grade software on accelerated timelines. We use AI to accelerate how we build, and we embed AI into what we build.
- What does 'built faster, built deeper' mean?
- Faster is how we work: AI-accelerated discovery, design, build and hardening compress delivery timelines. Deeper is what we deliver: complex domains, real architecture, security, observability and AI embedded at the core — systems designed to be extended for years, not replaced next quarter. Speed alone is commoditized; depth is the difference.
- How does Boostack deliver faster than traditional teams?
- We run a disciplined, AI-accelerated cycle of discovery, design, build and hardening. AI compresses every stage — from research and architecture to code generation and test coverage — without skipping engineering principles.
- What does 'AI-native' mean?
- AI-native means two things at Boostack. First, every engineer uses AI as a daily multiplier across discovery, design, code, tests and review. Second, we embed AI into the product itself — RAG portals, agents, copilots and AI-native workflows — not as bolt-ons.
- Who does Boostack work with?
- Enterprises modernizing core systems, scale-ups needing production-grade engineering, and innovation teams at established companies launching AI products. Typical sponsors are CTOs, VPs of Product and Innovation Directors.
- What's the technology stack?
- TypeScript, React and TanStack on the front end. Node and Python on the back end. Postgres, BigQuery and vector databases for data. Vertex AI, OpenAI, Anthropic and Google Gemini for AI. Google Cloud (Cloud Run, Cloud SQL, Security Command Center) for the platform.
- Where is Boostack based?
- Boostack Technologies LLC operates as a distributed senior team. We work in English and Spanish and partner with clients globally. Reach the team at support@boostack.co.