What services does Boostack offer?

Product engineering for new builds, AI systems and embedded AI features (RAG, agents, copilots), modernization of legacy enterprise systems, and platform engineering on Google Cloud.

Do you do design as well as engineering?

Yes. Discovery, UX, architecture and engineering are one continuous workflow at Boostack. The same senior team owns design and code, which removes handoff friction.

What about security and compliance?

Security is a Day-1 concern, not a phase. Our stacks are SOC 2, GDPR and HIPAA-ready, and we use Google Cloud Security Command Center, secret management and least-privilege IAM as defaults.

How do engagements start?

Send us a brief via the contact form or email support@boostack.co. We respond within one business day with how Boostack would approach it and propose a discovery session.

Services & Stack — Boostack

[ Services ]

What we build.

Three service lines. All executed with the same AI-native methodology and the same enterprise quality bar.

Service / 01

Enterprise Software Development

Complete, production-grade software. With AI where it creates real value.

Full-stack applications built end-to-end — from domain design to cloud deployment — with AI capabilities embedded where they belong: in the core product flows, not as afterthoughts.

Focused Build

Defined scope, core functionality, full production quality

Timeline: Multi-week build
Reference: $35K–$60K USD

Flagship

Full Product Build

Complete product with hardening, operations and AI integration

Timeline: Single delivery cycle
Reference: $100K–$200K USD

Every delivery includes

Full source code, design system, automated testing, cloud infrastructure (GCP), documentation and post-launch support.

Built with

React, TypeScript, Node.js, Python, PostgreSQL — plus the AI stack your product requires.

Service / 02

AI Systems & Agents

AI that works inside your product. Not alongside it.

We design and build AI systems where generative and deterministic capabilities are tightly integrated into core product functionality — knowledge systems, intelligent workflows, conversational interfaces and data pipelines that actually move the business.

Scoped AI systems · $25K–$70K USD

RAG & Knowledge Systems
Accurate, grounded, source-traceable AI responses at scale
Conversational AI
Multi-turn enterprise interfaces with context, memory and guardrails
Workflow Automation
AI-powered processes with human-in-the-loop controls where needed
Data & Analytics Agents
Extraction, synthesis and insight generation over large datasets
API & Integration Agents
Intelligent orchestration across enterprise systems

Service / 03

Digital Product Design

Design that ships. Not design that gets handed off.

Product discovery, UX research, flow design, prototypes and design systems — fully integrated with the engineering cycle, not as a separate phase that creates misalignment.

Product DiscoveryDiscovery sprint
UX Design (flows, wireframes, prototypes)Design phase
UI Design (visual design, design system)Design phase
Usability ValidationValidation sprint

[ The Difference ]

There are two ways to use AI in software. Most teams only use one.

Adding AI tools to your IDE makes developers faster. That's useful — but it's not the same as rebuilding your engineering practice around AI, and it's not the same as building a product where AI is a first-class part of the functionality. Boostack does both.

Dimension

Traditional + AI tools

Boostack AI-Native

AI in development
Autocomplete and code suggestions
Structured pipeline with quality gates governed by senior engineers
AI in the product
Add-on feature, bolt-on chatbot
Core functional layer — deterministic and generative working together
Quality posture
Quality treated as a phase
Scalable, secure and compliant from the first release
Delivery model
Long, multi-vendor programs
Small senior team operating with one disciplined cycle
What ships
MVP — stripped down, plans to rebuild later
Full functionality for the defined scope, ready for production
Technical debt
Accumulates; pushed to a future "v2"
Prevented by design; reviewed at every gate
Operations
Bolted on after launch
Logging, monitoring and runbooks built in from the start
Design ↔ engineering
Sequential handoffs
Continuous, fused workflow

The difference isn't the tools — anyone can install them. The difference is methodology: a disciplined build cycle, a structured way to embed AI into product functionality, and an unwillingness to ship software that isn't ready for production.

[ Technology ]

The stack we run in production.

Every technology listed here is in production in the products we build. Our baseline comes from Sequentia — our flagship product, built end-to-end with the Boostack methodology. Not a reference architecture. Not aspirational.

[ Part A ]

Application Stack

/ Frontend

Modern, accessible, internationalized. Multi-language UIs (EN, ES, PT, FR, DE), full theming, real-time state, and embedded data viz — one coherent design system.

Framework: React 18 + TypeScript
Build & tooling: Vite 5
State & data: TanStack Query v5, Zustand
UI components: shadcn/ui + Radix UI
Styling: Tailwind CSS v4
Forms & validation: React Hook Form + Zod
Internationalization: i18next — 5 languages in production
Data visualization: Recharts, D3
Animations: Framer Motion

/ Backend

TypeScript end-to-end, Zod validation at every API boundary, modular domain-organized service structure.

Runtime: Node.js 20 + TypeScript 5
Framework: Express.js — modular, domain-organized
Validation: Zod, end-to-end across all endpoints
Sessions & auth: Passport, OIDC/SSO, JWT, custom dual-gate RBAC
Security: Helmet, rate limiting, input sanitization, audit logging
API documentation: OpenAPI / Swagger

/ Data Layer

Polyglot persistence — each engine chosen for the access pattern it serves: relational, vector, graph, document, cache.

PostgreSQL: Primary relational — transactional data, tenant isolation
pgvector + HNSW: Vector embeddings and ANN search for RAG
Neo4j: Graph database for multi-hop knowledge reasoning (Graph RAG)
Redis: Distributed caching, rate limiting, cross-instance coordination
Firestore: Document storage and real-time data sync
BigQuery: Analytics and large-scale data processing

/ AI & Knowledge Layer

All LLM providers routed through an internal AI Gateway: intelligent routing, fallback, cost controls, per-workspace token budgets. No single-provider dependency.

LLM providers: Anthropic Claude, OpenAI GPT-4, Google Gemini, Mistral
Embeddings: OpenAI, Cohere, Voyage
RAG: Hybrid vector + BM25 with custom chunking and metadata enrichment
Graph RAG: Multi-hop reasoning over connected knowledge structures
Agent protocol: MCP — production server exposing KB resources and tools
AI Gateway: Internal control plane — routing, fallback, cost, prompt registry

/ Integrations

Knowledge sources: Zendesk, Confluence, SharePoint, Notion, Google Drive, web scraping, file/image/video ingestion
Outbound sync: Zendesk, Freshdesk, Intercom, Zoho Desk, Gorgias, HubSpot, GLPI, Ivanti, Aranda, BMC Helix
Communication: Slack, Microsoft Teams, email
Object storage: Google Cloud Storage (primary), AWS S3, Azure Blob
Billing: Stripe

/ Observability & Quality

Telemetry: OpenTelemetry — metrics, traces, structured logs with correlation IDs
Testing: Vitest, Testing Library, Supertest, in-memory PostgreSQL for DB-boundary tests
CI/CD: GitHub Actions — typecheck, tests, security scanning, CodeQL, tenant isolation, migration safety
Job processing: Durable PostgreSQL-backed job queue with retry, DLQ, concurrency control

[ Part B / Production Platform ]Pursuing Google Cloud Partner

Google Cloud is where our software lives.

For every product we deliver, Google Cloud is the production environment. Our default, our standard, and our area of deepest operational expertise. Compliance-ready data platforms, a mature AI infrastructure layer, and security tooling that satisfies the most demanding clients.

For development and rapid prototyping, we use specialized AI-accelerated tools. For production, it's GCP.

/ Compute

Right compute primitive per workload — serverless for most APIs, containers for complex workloads, managed clusters when scale demands it.

Cloud Run: Primary production target — containerized APIs, web apps, workers. Scales to zero.
Cloud Functions (2nd gen): Event-driven functions for webhooks, triggers, lightweight integrations
Google Kubernetes Engine: Workloads requiring fine-grained orchestration and autoscaling at scale
Cloud Run Jobs: Batch processing — ingestion pipelines, scheduled exports, data migrations

/ Data & Storage

Cloud SQL (PostgreSQL): Primary managed relational — automated backups, failover, read replicas
AlloyDB for PostgreSQL: Sub-10ms read latency alongside analytics
Cloud Spanner: Multi-region with strict transactional consistency
Firestore: Document storage and real-time sync with offline-capable SDKs
BigQuery: Enterprise analytics, product metrics, large-scale processing
Bigtable: High-throughput time-series, event logs, telemetry at scale
Memorystore (Redis): Managed Redis — caching, sessions, distributed rate limiting
Cloud Storage: Object storage — uploads, artifacts, exports, data lake staging

/ AI & Machine Learning

Integration depth between Vertex AI, Gemini, and the rest of GCP eliminates latency, auth, and compliance friction that multi-cloud AI architectures introduce.

Vertex AI: Managed ML — training, fine-tuning, evaluation, registry
Gemini API (via Vertex AI): Text, multimodal, function calling, long-context reasoning
Vertex AI Search: Enterprise RAG-capable semantic search with built-in grounding
Vertex AI Agent Builder: Conversational AI infrastructure with tool use and multi-turn memory
Document AI: Intelligent extraction — PDFs, forms, invoices, contracts
Natural Language AI: Entity extraction, classification, sentiment in production pipelines
Speech-to-Text / TTS: Voice I/O for conversational and contact center integrations
Translation AI: Multilingual support — LATAM and global enterprise deployments
Vision AI: Image processing, OCR, content moderation

/ Security & Compliance

Full GCP security stack from day one — not added post-launch.

IAM: Least-privilege — every service account has exactly the permissions it needs
Workload Identity Federation: Keyless authentication from CI/CD — no long-lived secrets
Secret Manager: Centralized secrets with versioning, audit logging, rotation
Cloud KMS: Customer-managed encryption keys across SQL, Storage, BigQuery, Pub/Sub
Security Command Center: Unified vulnerability, misconfiguration, threat posture
Cloud Armor: DDoS protection and WAF on every public-facing deployment
Binary Authorization: Only attested, verified container images reach production
VPC Service Controls: Data exfiltration prevention — perimeters around sensitive services
Cloud Audit Logs: Immutable audit trail across all services

/ DevOps & Delivery

Cloud Build: Managed CI/CD — build, test, deploy pipelines
Artifact Registry: Private container/package registry with vulnerability scanning
Cloud Deploy: Progressive delivery — canary, staged rollouts, automated rollback

/ Observability & Operations

Cloud Monitoring: Metrics, SLOs, uptime checks, dashboards
Cloud Logging: Structured JSON logs with correlation IDs
Cloud Trace: Distributed tracing across services and AI pipeline stages
Cloud Profiler: Continuous production profiling
Error Reporting: Automatic exception grouping and alerting
Pub/Sub: Managed event streaming and message bus
Cloud Tasks + Scheduler: Async task queues and managed cron for background workloads

/ Data Pipelines & Analytics

Dataflow: Batch and streaming transformations — ETL and embedding generation at scale
Cloud Composer (Airflow): Workflow orchestration for complex data pipelines
Looker Studio: Product analytics dashboards and operational reporting
BigQuery: Central analytics layer — cross-product, cross-tenant

/ GCP Production Coverage

ComputeCloud Run · Cloud Functions · GKE · Cloud Run Jobs
DataCloud SQL · AlloyDB · Spanner · Firestore · BigQuery · Bigtable · Memorystore · Cloud Storage
AI & MLVertex AI · Gemini API · Vertex AI Search · Agent Builder · Document AI · NLP · Speech · Translation · Vision
SecurityIAM · Workload Identity · Secret Manager · KMS · Security Command Center · Cloud Armor · Binary Authorization · VPC SC · Audit Logs
DevOpsCloud Build · Artifact Registry · Cloud Deploy
ObservabilityCloud Monitoring · Cloud Logging · Cloud Trace · Cloud Profiler · Error Reporting · Pub/Sub · Cloud Tasks · Cloud Scheduler
PipelinesDataflow · Cloud Composer · Looker Studio

[ FAQ ]

Services questions

What services does Boostack offer?: Product engineering for new builds, AI systems and embedded AI features (RAG, agents, copilots), modernization of legacy enterprise systems, and platform engineering on Google Cloud.
Do you do design as well as engineering?: Yes. Discovery, UX, architecture and engineering are one continuous workflow at Boostack. The same senior team owns design and code, which removes handoff friction.
What about security and compliance?: Security is a Day-1 concern, not a phase. Our stacks are SOC 2, GDPR and HIPAA-ready, and we use Google Cloud Security Command Center, secret management and least-privilege IAM as defaults.
How do engagements start?: Send us a brief via the contact form or email support@boostack.co. We respond within one business day with how Boostack would approach it and propose a discovery session.

[ Project consultation ]

Tell us about what you want to build.

Share the company, what you want to build and the outcome you're after. We'll come back within one business day with how Boostack would approach it.