AI & Automation · Production-grade

AI agents that actually ship revenue

Custom AI agents, MCP servers, RAG systems, OpenAI & Anthropic integrations: engineered for production, with eval frameworks, guardrails, and a clear ROI.

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Trusted by 250+ brands across the US, UK, UAE & Europe

01 / CAPABILITIES

Eight AI capabilities that ship production revenue.

Not demos. Not toys. We build AI agents and automations with evals, guardrails, observability, and clear ROI: on Anthropic, OpenAI, and open-source stacks.

01

Custom AI agents

Multi-step agents for support, sales, ops: tool-using, memory-aware, eval-backed. Claude + GPT + custom toolchains.

02

MCP server development

Model Context Protocol servers that expose your data + APIs to Claude, ChatGPT, and any AI client: secure, scalable, eval-backed.

03

RAG & search

Vector search with pgvector or Pinecone, hybrid retrieval, reranking, chunking strategies, with eval-driven accuracy.

04

Document AI

Extract structured data from PDFs, contracts, invoices, forms: with vision models, accuracy guarantees, and human-in-the-loop.

05

Workflow automation

n8n, Make, Zapier, or custom Node + LangChain pipelines for complex internal automations.

06

Eval frameworks

Promptfoo, Braintrust, LangSmith. We set up CI evals so AI changes don’t regress accuracy.

07

Guardrails + safety

Prompt injection detection, output filtering, PII redaction, rate limiting, production-grade safety controls.

08

Observability

Token usage, latency, accuracy drift, hallucination detection, with dashboards and alerts.

02 / PROCESS

How a AI engagement runs.

Anyone can demo an agent. These four stages exist to get one into production and keep it there.

  1. Stage 1

    Use-case scoping and baseline

    Stage 1

  2. Stage 2

    Prototype with evals

    Stage 2

  3. Stage 3

    Harden with guardrails

    Stage 3

  4. Stage 4

    Deploy and monitor

    Stage 4

03 / OUR WORK

Recent work from our portfolio.

Live stores, apps and platforms we have built and run. Case studies open on this site; other projects open the live build in a new tab.

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2×

Conversion uplift

typical after a CRO-led rebuild

60%

Faster load times

median improvement at launch

40%

Lower cost

against an in-house team of the same size

99.9%

Uptime

on Shopify infrastructure

04 / PRICING

Transparent pricing. Pick the model that fits your stage.

Fixed-scope projects, ongoing retainers, or full-time dedicated teams. Whatever the engagement, you'll see the price before you sign: no hourly creep, no surprise change orders.

Project

Fixed-scope build

$5K – $250K

Fixed timeline · Milestone-billed

Best for: Store launches, redesigns, migrations, mobile apps, MVPs

  • Fixed SOW & written deliverables
  • 14–60 day hypercare included
  • Clean code & documentation handover
  • Easy off-ramp, no lock-in

Retainer

Monthly partnership

$1.2K – $12K / mo

15–150 hrs/month · Month-to-month

Best for: Established stores running CRO, A/B tests, & ongoing improvements

  • Dedicated PM + senior engineer
  • Sprint cadence with weekly demos
  • 2–8 A/B tests per month
  • Unused hours roll 50% forward

Dedicated team

Full-time pod

$8K – $40K / mo

1–8 engineers · Embedded in your roadmap

Best for: In-house augmentation, Shopify Plus brands, multi-region scaling

  • Senior engineers full-time on your team
  • Direct Slack & daily standups
  • Reports to your CTO/Eng Lead
  • Signed SLA & SOC2-aligned process

05 / WHY BRANDS STAY

Six reasons teams stay with us once it is live.

Most of our AI work is repeat business or referral. These are the reasons clients give when we ask why.

Shopify Partner since 2018

Certified, with direct partner support channels when a build needs them.

250+ stores shipped since 2019

Seven years of Shopify-specific pattern recognition, not general web work.

A dedicated PM on every build

One accountable owner, weekly written updates, no account-manager telephone game.

Conversion-first, not pixel-first

Design decisions are argued from revenue impact, and measured after launch.

Transparent, fixed-scope pricing

Written scope and a fixed number before work starts. No surprise line items.

You own the code, always

Clean handover, documented, on your repos. Leaving is never held over you.

06 / TECH STACK

What we build AI systems with.

Model-agnostic where it matters, with the evaluation and observability that separate a production system from a demo.

Models & agents

  • Liquid
  • Online Store 2.0
  • Hydrogen
  • React
  • Next.js
  • JavaScript
  • TypeScript

Retrieval

  • Node.js
  • PHP
  • Python
  • GraphQL
  • Storefront API
  • Shopify Functions

Evaluation

  • MySQL
  • PostgreSQL
  • MongoDB

Runtime & ops

  • AWS
  • Google Cloud
  • Kubernetes

07 / FAQ

Questions we hear before every AI & Automation engagement.

Honest answers, including the ones most agencies dodge.

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What kind of AI do you build?

Production AI: agents, MCP servers, RAG systems, document AI, custom automations. Not demos, revenue-tied implementations with evals and observability.

OpenAI or Anthropic?

Both. We pick the right model for the task. Claude for reasoning + writing, GPT-4 for tool use, Haiku / Mini for cost-sensitive paths. Open-source where appropriate.

How do you measure AI quality?

Eval frameworks (Promptfoo, Braintrust, LangSmith) with curated test sets, regression CI, accuracy tracking, hallucination detection, not vibes.

What’s an MCP server?

Model Context Protocol: Anthropic’s open spec that lets AI agents call your tools and access your data. We build production MCP servers with auth, rate limits, observability.

Can AI agents handle real customer support?

Yes: with guardrails, escalation paths, and human-in-the-loop. We’ve shipped agents handling 40–75% of tier-1 support volume.

Cost?

AI sprint: $8K–$30K. Agent build: $40K–$150K. MCP server: $25K–$80K. RAG system: $20K–$80K. Plus token costs (usually $200–$5K/mo).