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Services

Production AI, RAG systems, and the SaaS infrastructure around them.

Four service tracks. One studio. Engagements end with code and infrastructure your team owns and can extend, not a vendor lock-in. Every track starts with the free strategy call or the 48-hour AI audit.

AI Strategy Consulting

AI Strategy & Roadmap

Decide what AI to build (and not build) before you spend the budget.

A focused engagement for product teams considering AI. We pressure-test the user problem, evaluate data readiness, model the cost and latency at scale, and map a sequence of features ranked by feasibility and impact. You leave with a written roadmap your engineering team can act on.

What you get

  • Free 30-minute strategy call (no card, no follow-up sequence)
  • Written 48-hour AI Integration Audit on request
  • Prioritised feature list with build/buy/skip recommendations
  • Cost-at-scale model for the recommended AI stack

Frequently asked questions

View all FAQs

We run a tight build loop that keeps scope, cost, and risk visible. Most engagements start with a short discovery to align on the outcome, constraints, and success metrics. Then we ship in weekly iterations with demos, clear milestones, and written updates so your team always knows what’s next.

  • Kickoff: goals, users, constraints, and success metrics
  • Plan: milestone-based roadmap + risks + assumptions
  • Build: weekly shipping cadence with demos and changelogs
  • Harden: QA, performance, monitoring, and launch checklist
  • Handoff: docs, repo access, runbooks, and knowledge transfer

Pricing depends on the scope, speed, and reliability you need. We can work as a fixed-scope milestone (when requirements are clear) or as a weekly retainer (when you want speed and flexibility). In both cases, you get a written plan and a transparent breakdown of what’s included.

  • Typical drivers: #workflows, integrations, data complexity, and quality bar
  • AI-specific drivers: evaluation, observability, safety/guardrails, and cost controls
  • We’ll share a range after a short call and a written scope before we start

Timelines depend on complexity and how quickly we can validate requirements and access data. We’ll propose milestones early so you can make decisions with real checkpoints instead of waiting until the end to see progress.

  • Discovery + plan: a few days to 1 week
  • First shippable milestone: 1–3 weeks (depending on access + scope)
  • Production hardening: ongoing (tests, monitoring, performance, security)

You get working software, not a slide deck. We deliver production-ready code, documentation, and operational basics so your team can run and extend what we ship. If you already have engineers, we prioritize clean handoff and long-term maintainability.

  • Code in your repo (or a repo you control), with PR-based delivery
  • Documentation: setup, architecture notes, and key decisions
  • Runbooks: how to deploy, monitor, and troubleshoot
  • Handoff session with your team (and follow-up support if needed)

AI/ML Engineering

Machine Learning, RAG & LLM Systems

Production RAG, semantic search, multi-LLM routing, and AI agents that are eval'd, observable, and cost-controlled.

We build the AI systems behind real products. RAG over millions of documents (DecoverAI: legal discovery on Pinecone + AWS EKS, helped raise $2M+ seed). Multi-LLM routers that pick the right model per task. Agent systems with proper evals, observability, and cost ceilings. Every system ships with monitoring, regression suites, and a runbook your team owns.

What you get

  • RAG pipeline (vector DB, retrieval, re-ranking, eval harness)
  • Multi-LLM routing with cost and latency budgets
  • Production observability (Langfuse, OpenTelemetry, custom dashboards)
  • Hallucination guardrails and validation layers
  • Documentation and team handoff

Frequently asked questions

View all FAQs

We run a tight build loop that keeps scope, cost, and risk visible. Most engagements start with a short discovery to align on the outcome, constraints, and success metrics. Then we ship in weekly iterations with demos, clear milestones, and written updates so your team always knows what’s next.

  • Kickoff: goals, users, constraints, and success metrics
  • Plan: milestone-based roadmap + risks + assumptions
  • Build: weekly shipping cadence with demos and changelogs
  • Harden: QA, performance, monitoring, and launch checklist
  • Handoff: docs, repo access, runbooks, and knowledge transfer

Yes — we can sign an NDA. We also design for least privilege, avoid leaking secrets into logs, and keep environments separated. If you have a security baseline (SOC2 controls, SSO, audit logs, etc.), we’ll align the delivery to it.

  • Secrets management: env separation and no credentials in the repo
  • Access: least privilege for services and team accounts
  • Data handling: secure storage, retention expectations, and auditability where needed

Timelines depend on complexity and how quickly we can validate requirements and access data. We’ll propose milestones early so you can make decisions with real checkpoints instead of waiting until the end to see progress.

  • Discovery + plan: a few days to 1 week
  • First shippable milestone: 1–3 weeks (depending on access + scope)
  • Production hardening: ongoing (tests, monitoring, performance, security)

You get working software, not a slide deck. We deliver production-ready code, documentation, and operational basics so your team can run and extend what we ship. If you already have engineers, we prioritize clean handoff and long-term maintainability.

  • Code in your repo (or a repo you control), with PR-based delivery
  • Documentation: setup, architecture notes, and key decisions
  • Runbooks: how to deploy, monitor, and troubleshoot
  • Handoff session with your team (and follow-up support if needed)

SaaS Product Engineering

Full-Stack SaaS Development

Production SaaS infrastructure on Next.js, Node, Python, MongoDB/Postgres, and AWS/GCP, built to scale.

We build the full product around the AI: auth, billing, dashboards, multi-tenancy, admin tooling, and the surrounding workflows. 50+ projects shipped across SaaS, marketplaces, and internal tools, with 1M+ cumulative users in production.

What you get

  • Frontend (Next.js / React) and backend (Node / Python)
  • Database design (Postgres, MongoDB) with migrations
  • Auth, billing, multi-tenancy, RBAC
  • CI/CD on AWS or GCP with environment isolation
  • Performance budgets and Core Web Vitals targets

Frequently asked questions

View all FAQs

We run a tight build loop that keeps scope, cost, and risk visible. Most engagements start with a short discovery to align on the outcome, constraints, and success metrics. Then we ship in weekly iterations with demos, clear milestones, and written updates so your team always knows what’s next.

  • Kickoff: goals, users, constraints, and success metrics
  • Plan: milestone-based roadmap + risks + assumptions
  • Build: weekly shipping cadence with demos and changelogs
  • Harden: QA, performance, monitoring, and launch checklist
  • Handoff: docs, repo access, runbooks, and knowledge transfer

We choose the stack based on your constraints (team skills, budget, compliance, time-to-market). For most modern web products, we default to a typed Next.js setup with a reliable database, solid CI/CD, and observability from day one.

  • Frontend: Next.js + TypeScript + Tailwind
  • Backend: Node/Next APIs or Python (FastAPI) depending on the product
  • Data: Postgres/MongoDB, plus object storage (S3/GCS) as needed
  • AI: pragmatic LLM/RAG architecture with evals and tracing (when applicable)

You do. The code we ship is delivered into your repo or transferred to you at the end of the engagement. We aim to avoid vendor lock-in by documenting the system and using standard tooling your team can maintain.

  • Repo access and ownership stays with you
  • Documentation + runbooks included for maintainability
  • We can support a full handoff to your internal team

Yes. After launch, we can stay involved for iteration, monitoring, and reliability improvements. Some teams prefer a small monthly retainer; others keep us on-call for specific releases or performance/security hardening.

  • Bug fixes and reliability work after launch
  • Performance tuning and cost optimization (especially for AI features)
  • Feature iterations and roadmap support

Data Engineering

Data Engineering & Pipelines

ETL/ELT, vector pipelines, and the data plumbing AI features need to actually work.

Most AI features fail not because of the model but because of the data feeding it. We build the pipelines: ingestion, normalisation, embedding generation, vector storage, refresh schedules, and quality monitors. Designed for ongoing maintenance, not one-shot demos.

What you get

  • Ingestion pipelines (batch and streaming)
  • Embedding generation and vector store population
  • Data quality monitors and drift detection
  • Backfills, replays, and schema evolution support

Frequently asked questions

View all FAQs

We run a tight build loop that keeps scope, cost, and risk visible. Most engagements start with a short discovery to align on the outcome, constraints, and success metrics. Then we ship in weekly iterations with demos, clear milestones, and written updates so your team always knows what’s next.

  • Kickoff: goals, users, constraints, and success metrics
  • Plan: milestone-based roadmap + risks + assumptions
  • Build: weekly shipping cadence with demos and changelogs
  • Harden: QA, performance, monitoring, and launch checklist
  • Handoff: docs, repo access, runbooks, and knowledge transfer

Yes — we can sign an NDA. We also design for least privilege, avoid leaking secrets into logs, and keep environments separated. If you have a security baseline (SOC2 controls, SSO, audit logs, etc.), we’ll align the delivery to it.

  • Secrets management: env separation and no credentials in the repo
  • Access: least privilege for services and team accounts
  • Data handling: secure storage, retention expectations, and auditability where needed

You get working software, not a slide deck. We deliver production-ready code, documentation, and operational basics so your team can run and extend what we ship. If you already have engineers, we prioritize clean handoff and long-term maintainability.

  • Code in your repo (or a repo you control), with PR-based delivery
  • Documentation: setup, architecture notes, and key decisions
  • Runbooks: how to deploy, monitor, and troubleshoot
  • Handoff session with your team (and follow-up support if needed)

Timelines depend on complexity and how quickly we can validate requirements and access data. We’ll propose milestones early so you can make decisions with real checkpoints instead of waiting until the end to see progress.

  • Discovery + plan: a few days to 1 week
  • First shippable milestone: 1–3 weeks (depending on access + scope)
  • Production hardening: ongoing (tests, monitoring, performance, security)

Not sure which one fits?

Start with the strategy call. 30 minutes, free, no follow-up sequence. You leave with a clear read on whether AI is the right move and what to do first.