Tools, stack & AI capabilities

We are tool-agnostic on principle — we pick what fits your constraints, team, and risk profile. Below is a representative map of what we ship with regularly, especially for AI-enabled and high-traffic products.

Intelligence layer

AI that ships — not slides

Clients stay when they see judgment: model choice, evals, guardrails, and cost control. We treat LLMs as components in a system — with logging, rollback, and clear ownership — not as magic text boxes taped onto legacy UX.

Start from outcomes, not models

We tie AI work to measurable tasks: support deflection, sales qualification, document extraction, or internal copilots — with baselines before models.

Safety and privacy by design

Data classification, retention, and regional constraints are mapped early — especially for health, finance, and education clients.

Cost-aware architecture

Caching, batching, smaller models for subtasks, and fallbacks when providers rate-limit — so bills do not surprise finance.

Human-in-the-loop when stakes are high

Escalation paths, review queues, and override rules — so automation helps operators instead of boxing them in.

AI, LLMs & intelligent automation

Modern product work increasingly includes models, evals, and guardrails — not a single “ChatGPT button.” We help you choose patterns that survive real traffic and compliance review.

OpenAI API (GPT-4 class models, assistants, structured outputs)
Anthropic Claude · Amazon Bedrock · Azure OpenAI
LangChain / LangGraph-style orchestration (when complexity warrants)
Vector DBs: Pinecone, Weaviate, pgvector, Redis vector search
Embeddings, reranking, hybrid search (keyword + semantic)
Evaluation: prompt regression suites, offline eval sets, human review loops
Guardrails: PII redaction, policy filters, jailbreak testing
Observability: token/cost dashboards, latency SLOs, failure sampling

Frontend & design-to-code

React 18+ · Next.js (App Router) · TypeScript
Tailwind CSS · Radix UI / headless patterns
Storybook for component documentation
Figma → code workflows and design tokens
Accessibility: WCAG-oriented patterns, axe, manual keyboard testing
Core Web Vitals: image optimization, font loading, bundle analysis

Backend, APIs & realtime

Node.js (Express/Fastify) · serverless functions
REST & GraphQL · OpenAPI documentation
PostgreSQL · Redis · message queues (SQS, Rabbit patterns)
WebSockets / SSE for live dashboards and ops tools
Auth: OAuth2/OIDC, JWT refresh, session hardening
Idempotency, rate limits, and abuse protection for public APIs

Mobile

React Native (new architecture where applicable)
Flutter & Dart for expressive UI
Push notifications · deep links · app links
Offline sync patterns for field / technician apps
Store release: Play Console & App Store Connect workflows

Cloud, DevOps & quality

AWS · GCP · Vercel — fit-for-purpose hosting
Docker · CI/CD (GitHub Actions, etc.)
Infrastructure as code where teams benefit
Secrets management · least-privilege IAM
Testing: unit, integration, e2e (Playwright/Cypress) for critical paths
Error tracking: Sentry or equivalent

Data, analytics & experimentation

Google Analytics 4 · Tag Manager (server-side when needed)
Product analytics: Mixpanel / Amplitude-style event models
Warehouse patterns: BigQuery-ready exports when scale demands
A/B testing and feature flags for safe rollouts
Attribution models with honest limits documented for leadership

Growth, SEO & paid media

Technical SEO audits · schema · sitemaps · internationalization
Content clusters & editorial calendars tied to funnel stages
Google Ads · Meta Ads — creative iteration with clear hypotheses
Conversion tracking and offline conversion imports where applicable
CRM hooks: HubSpot-style integrations, lead routing SLAs

Crypto / fintech-adjacent (where relevant)

Wallet UX patterns · chain-agnostic product thinking
KYC vendor integration patterns (vendor-specific)
Admin, risk, and treasury dashboards — role separation
High-readiness monitoring for trading-adjacent workloads

How we choose

Selection criteria include: team familiarity, hiring market in your region, compliance posture, latency targets, total cost of ownership, and exit strategy (vendor lock-in). If a tool is trendy but wrong for your stage, we say so — and propose a leaner path.

For AI specifically, we document model versions, data handling, and evaluation metrics in the same place as your product requirements — so legal, security, and engineering read one story.

Discuss your stack →

Want this rigor on your roadmap?

Send a brief — we will challenge assumptions constructively and propose a phased plan.