Deep Technical Command of the Platforms
Behind Enterprise AI
We chose to go deep on two platforms instead of spreading across ten. Between n8n and Google Vertex AI, we've hit the same problems often enough to know their limits, their common failure points, and which architecture decisions hold up over time. That's what we deliver: less trial and error, more judgment.
n8n Workflow Automation
The open-source automation platform we reach for on complex enterprise workflows, for the control it gives over every node and integration. We've spent years focused on n8n, so we know its quirks well.
- ✓ 500+ native integrations
- ✓ Self-hosted for data control
- ✓ Unlimited workflow complexity
- ✓ Custom code nodes
- ✓ Enterprise-grade error handling
Google Vertex AI
Google Cloud's unified AI platform for training, deploying, and managing custom ML models at enterprise scale. We are a certified Google Cloud partner.
- ✓ Custom model training
- ✓ MLOps pipeline automation
- ✓ Auto-scaling model serving
- ✓ Explainable AI built-in
- ✓ Enterprise security & compliance
Why We Focus Instead of Diversify
Generalist agencies say they work with everything. We'd rather know two complementary platforms in depth, so we skip the on-the-job learning curve that tends to inflate timelines.
Faster Delivery
We already have the patterns built and the components ready to reuse, so we're not relearning the platform on every new project.
Fewer Surprises
Years on both platforms means we've run into most of the edge cases already. We know which pitfalls to route around before they stall a project.
Better Architecture
We know which architecture decisions hold up under growth and which ones need rework in six months. We pick the first kind.
The Full Technology Stack We Use
Everything here has run in production, not just in a demo. We picked each piece for how it behaves under an actual client's load and constraints, not for what its marketing page promises.
AI & Large Language Models
Foundation models and AI frameworks we use to automate decisions and tasks that used to need manual review
Google Vertex AI
ML platform for training and deploying models on GCP. Core AI/ML infrastructure for risk modeling, scoring engines, and production-grade custom model pipelines.
- Custom model training & MLOps
- Auto-scaling model serving
- Explainable AI built-in
- Enterprise security & compliance
Gemini (Google)
Google's multimodal AI model, built to work natively with the rest of GCP. We treat it as an extension of Vertex AI for document analysis, chat interfaces, and code generation.
- Multimodal input: text, image, code
- Native Vertex AI integration
- Document & contract analysis
- Long-context reasoning (1M tokens)
Claude / Anthropic
Enterprise-grade LLM known for safety, long context windows, and precise reasoning. Ideal for credit document review, contract analysis, and compliance-sensitive workflows.
- 200K token context window
- Superior instruction following
- Low hallucination rate
- Data privacy & enterprise SLAs
LangChain
Open-source framework for building LLM-powered applications. Orchestrates AI chains for multi-step evaluation workflows, RAG pipelines, and structured output generation.
- LLM chain orchestration
- RAG pipeline construction
- Memory & stateful conversations
- 100+ LLM & vector DB integrations
CrewAI
Multi-agent framework for autonomous task execution. Specialized agents collaborate to automate complex KYC checks, document review, and multi-step data enrichment.
- Role-based agent specialization
- Inter-agent task delegation
- Tool use & web browsing
- KYC & compliance automation
RAG: Retrieval-Augmented Generation
Architecture pattern combining semantic search with LLMs for grounded, accurate answers. Enables AI to query proprietary financial data and documents while maintaining factual precision.
- Query private enterprise data
- Reduce LLM hallucinations
- Semantic document retrieval
- Integrates with any vector DB
OpenAI / GPT-4o
The most recognized AI brand outside technical circles, with multimodal capabilities: text, image, audio, and vision in one model. It has the largest catalog of third-party plugins and tends to be the easiest sell to non-technical procurement teams.
- Multimodal: text, image, audio, vision
- Largest catalog of third-party plugins
- Function calling & structured outputs
- Wide enterprise recognition & trust
Automation & Workflow
Integration engines and orchestration platforms we use to connect systems, move data between them, and automate processes that used to be manual
n8n Automation
Open-source workflow automation platform with self-hosting, unlimited executions, and full code node support. The backbone of enterprise integration architectures we build.
- 500+ native integrations
- Self-hosted, no per-task pricing
- AI agent & LLM nodes built-in
- Custom code in JS or Python
Make.com (Integromat)
Visual no-code automation platform with 1,500+ app integrations. Complements n8n for rapid merchant-facing automations, onboarding flows, and client-managed scenarios.
- 1,500+ app integrations
- Visual drag-and-drop builder
- Webhooks & real-time triggers
- Ideal for business-user automations
MCP: Model Context Protocol
Anthropic's open standard for connecting AI agents to external tools and data sources. Enables AI systems to interact with APIs, databases, Slack, and internal services through a standardized interface.
- Standardized AI-to-tool connectivity
- Works across Claude, GPT-4, Gemini
- Connect AI to internal APIs
- Extensible tool definitions
Cloud & Infrastructure
Cloud platforms and infrastructure tooling we use to deploy, scale, and keep production workloads running
Google Cloud Platform (GCP)
Cloud suite covering compute, storage, networking, and AI. It's where most of what we build actually lives, backed by certified expertise in Cloud Run, Cloud SQL, Pub/Sub, and the rest of the GCP AI portfolio.
- Cloud Run & GKE for containerized workloads
- Cloud SQL & BigQuery for data
- IAM, VPC & enterprise security
- Certified Google Cloud partner
Docker
Containerization platform for consistent, reproducible deployments across environments. Every API, microservice, and automation tool we deliver gets containerized, so it's portable across environments and simpler to operate.
- Environment parity: dev to prod
- Multi-stage optimized builds
- Docker Compose for local dev
- Integrates with GKE & Cloud Run
Kubernetes (GKE)
Container orchestration for scaling workloads. When your API infrastructure outgrows single-container deployments, GKE delivers auto-scaling, rolling updates, and zero-downtime deployments.
- Auto-scaling based on traffic
- Rolling deployments, zero downtime
- Managed via GKE on GCP
- Horizontal pod autoscaler (HPA)
Terraform
Infrastructure as code: declarative provisioning that keeps environments reproducible and version-controlled. Every cloud resource is defined in code, so it's auditable and doesn't quietly drift from what's expected.
- Declarative infrastructure definition
- Version-controlled cloud resources
- Multi-cloud provider support
- State management & drift detection
Data, Analytics & Vector Search
Warehouses, analytics platforms, and vector databases for AI-powered search, BI dashboards, and real-time data pipelines
BigQuery
Google's serverless data warehouse for large-scale analytics. Lets us analyze transaction patterns, portfolio data, and risk metrics at petabyte scale using SQL, without managing a single cluster.
- Serverless, no cluster management
- Native ML with BigQuery ML
- Real-time streaming ingestion
- Connects natively to Looker & Vertex
Pinecone
Managed vector database for AI-powered semantic search and retrieval. We use it in RAG pipelines and semantic search over financial documents, client profiles, and unstructured data.
- Fully managed vector infrastructure
- Sub-10ms similarity queries
- Scales to billions of vectors
- Native LangChain & LlamaIndex support
Looker / Looker Studio
Google's BI and data visualization platform. We use it to build interactive dashboards for portfolio performance, merchant analytics, and risk KPIs, directly on top of BigQuery and Cloud SQL data.
- Native BigQuery integration
- Embedded analytics in any app
- Real-time data dashboards
- LookML semantic data layer
Apache Kafka
Distributed event streaming platform for real-time data pipelines. Enables real-time transaction monitoring, fraud detection event streams, and high-throughput data ingestion architectures.
- Millions of events per second
- Durable, replayable event log
- Consumer group fan-out patterns
- Integrates with BigQuery & GCS
Development & APIs
Frameworks and standards for building, documenting, and deploying high-performance APIs and backend services
FastAPI / Python
Python framework for building high-performance APIs. It's our default backend for AI-powered endpoints: scoring services, LLM proxies, and enrichment APIs, all with OpenAPI docs generated automatically.
- Async-first, high throughput
- Auto-generated OpenAPI docs
- Native Pydantic data validation
- Ideal for ML model serving
Swagger / OpenAPI
Industry standard for documenting and defining API contracts. Every API we build ships with interactive Swagger UI, so your team and partners can explore, test, and integrate it without coming back to us for help.
- Interactive API explorer
- Machine-readable contract (OpenAPI 3.x)
- Auto-generated client SDKs
- Used in production on Cloud Run
Supabase
Open-source Firebase alternative with Postgres, authentication, and real-time subscriptions. Accelerates backend development for portal features and dashboard applications with a managed, scalable stack.
- Postgres with real-time subscriptions
- Built-in auth & row-level security
- Auto-generated REST & GraphQL APIs
- Edge functions for serverless logic
Next.js / React
Full-stack React framework for building high-performance web applications with server-side rendering and static generation. Ideal for merchant portals, documentation sites, and data-rich dashboard interfaces.
- Server-side & static rendering
- App Router with React Server Components
- Built-in API routes & middleware
- Optimized for data-driven UIs
Security & Compliance
Authentication, authorization, and protection layers we use to meet enterprise security requirements across APIs and data pipelines
OAuth 2.0 / JWT
Industry-standard protocols for authentication and authorization. Every API we deliver uses OAuth 2.0 flows with JWT validation, with tokens that expire and permissions scoped per integration.
- Stateless JWT authentication
- Scope-based authorization
- Token rotation & refresh flows
- Compatible with all major identity providers
Google Cloud Armor
DDoS protection and Web Application Firewall for cloud workloads. Protects API endpoints and web portals from volumetric attacks, bot abuse, and OWASP Top 10 vulnerabilities at the edge.
- DDoS mitigation at GCP edge
- OWASP Top 10 WAF rules
- Geo-based access control
- Adaptive threat intelligence