Solutions
Systems we build. Systems you run.
Automation, assistants, workflows, agents, integrations, and RAG systems — designed, built, governed, and handed off to your team. Production-grade, operable, ours to build, yours to operate.
01
Automation
Rule-based execution
02
Assistants
Role-specific copilots
03
Workflows
Multi-step chains
04
Agents
Autonomous systems
05
Integrations
Cross-system connectivity
06
RAG Systems
Knowledge retrieval
Six Build Types · One Methodology
Our Position
Not demos. Not prototypes. Systems.
Most AI vendors ship impressive demos that never make it to production. Most AI consultancies ship architecture diagrams that never become code. We ship running systems — deployed, observable, governed, and handed off to your team with documentation.
Every Solutions engagement ends the same way: systems you operate, not systems we operate for you.
Build Types
Six kinds of systems. One spine.
Every system we build falls into one of six canonical types. Every type shares the same production-grade spine: governance, observability, documentation, and handoff.
01
Build Type 01 · Automation
Rule-based execution at scale.
Deterministic systems that execute defined tasks without human intervention. Triggered by events, schedules, or inbound data. Reliable, auditable, fast — built on tools like n8n, Make, Zapier, or custom code depending on your stack.
What We Build
- Event-triggered workflows with deterministic logic
- Scheduled batch processes with error handling and retries
- Full observability and audit logging
Typical Use Cases
Automation · Invoice Pipeline
Executions Today: 342 · Errors: 2
02
Build Type 02 · Assistants
Role-specific copilots grounded in your knowledge.
Custom AI assistants tuned to a specific role or task. Grounded in your documents, policies, and processes. Deployed where your teams already work — Slack, Teams, email, custom UIs — or built as standalone GPTs and Claude Projects.
What We Build
- Custom GPTs and Claude Projects with company knowledge grounding
- Assistants deployed inside existing tools or as standalone interfaces
- Governance-ready: logged, rate-limited, evaluated, auditable
Typical Use Cases
Sales Copilot · Grounded
Sources: 4 Docs
03
Build Type 03 · Workflows
Multi-step AI chains that connect the work.
Chained processes where each step adds AI-powered judgment. A contract flows through extraction → classification → risk review → routing. Each step has its own model, its own governance, and its own metrics. Orchestrated end-to-end.
What We Build
- Multi-step chains combining AI models, tools, and human review
- Human-in-the-loop checkpoints at defined decision points
- End-to-end observability with per-step metrics
Typical Use Cases
Workflow · Contract Review
01
Intake
02
Classify
03
Extract
04
Review
05
Route
◆ Human Checkpoint at Step 04
68% Complete
04
Build Type 04 · Agents
Goal-oriented systems with reasoning and memory.
Autonomous agents that plan, execute, and adapt. Given a goal and a set of tools, they decide the sequence. Built on frameworks like LangGraph, CrewAI, or n8n's agent nodes. Deployed only where governance tier permits autonomous action.
What We Build
- Goal-oriented agents with tool access and memory
- Multi-agent orchestration with human supervision checkpoints
- Strictly gated deployment based on governance tier
Typical Use Cases
Agent · Goal-Oriented
Tier 3 · Gated05
Build Type 05 · Integrations
The plumbing that makes AI actually work.
No AI system delivers value if it can't reach your data, write to your systems, or listen to your events. We build the integration layer — APIs, data pipelines, event streams, webhook handlers — that connects AI to your operational reality.
What We Build
- API integrations connecting AI systems to your stack (CRM, ERP, ITSM, data warehouses)
- Event-driven pipelines with queues, retries, and dead-letter handling
- Secure authentication and authorization across internal and third-party systems
Typical Use Cases
Integration Layer · Hub & Spoke
API Calls Today: 12,847
06
Build Type 06 · RAG Systems
Retrieval-grounded AI your team can trust.
Retrieval-Augmented Generation systems that ground AI responses in your actual knowledge base — policies, SOPs, past decisions, product documentation. Accurate, cited, auditable. The foundation for reliable enterprise AI at scale.
What We Build
- Vector database setup with embeddings tuned to your content
- Retrieval logic with semantic + hybrid search and re-ranking
- Citation-backed outputs with answer confidence scoring
Typical Use Cases
RAG System · Knowledge Grounded
In Production
What that looks like, by function.
The build types aren't abstract. Here's the kind of work we ship into real functions — the use case is chosen with you, against your data and your regulatory context.
Finance
- Invoice OCR for Accounts Payable
- Expense report classification
- Supplier contract review
Sales
- Devis & quote generation
- RFP response drafting
- CRM enrichment from email
HR
- Ticket triage and routing
- CV screening with audit trail
- Policy Q&A assistant
Customer Ops
- Email classification & drafting
- Knowledge base assistant
- Escalation prediction
What every engagement leaves behind
The Foundation
Readiness lens, data map, and prompt library.
The System
Assistant, prototype, and agent with pilot metrics.
The Operating Plan
Tool selection ADR, integration plan, scored portfolio.
The Handoff Pack
System card, playbook, monitoring plan, roadmap.
The Spine
Every system, same backbone.
The build type varies. The production-grade foundation doesn't. Every system we deliver passes through the same four gates before it ships.
01
Governance
Every system opens a Governance Case. Controls, evidence, and evaluation criteria defined before build starts.
02
Observability
Every output logged. Every decision traceable. Every system visible via dashboard from day one of production.
03
Documentation
Architecture diagrams, runbooks, and operator guides delivered with every system. Your team inherits knowledge, not tickets.
04
Handoff
Structured handoff to your operators. Knowledge transfer sessions, shadowing, and 30-day support before we disengage.
How We Build
Three engagement shapes.
Different build scopes need different engagement models. We structure around the system being built — not around our billing preferences.
Shape 01
Focused build
A single system, end-to-end. Scope, build, govern, deploy, handoff. Tight and focused. Ideal for organizations with a clear priority use case or a specific AI initiative already scoped.
Best For
Clear single-system priorities
Timeline
6–12 weeks per system
Shape 02
Co-build sprint
We embed with your team and build together. Mixed squad of our builders and yours. Your team learns our methodology by doing — ready to continue building solo after the engagement ends.
Best For
Teams building internal AI capability
Timeline
8–16 weeks · variable
Shape 03
Portfolio delivery
Multiple systems in parallel across functions. We run a small delivery team shipping 3–6 systems over a program cycle, coordinated end to end. Best for organizations with an Advisory roadmap already in hand.
Best For
Transformation programs with multi-system scope
Timeline
3–6 months · concurrent delivery
Built to Run
Every system we build is yours to operate.
We don't hand you a black box. Every system we ship is scored on our readiness framework, governed with EU-aware controls, and documented so your team can run it without us.
Scored against our readiness framework
Every system we ship is classified and scored on the House of Ichigo framework — so you know where it sits, what it's worth, and how it ranks against your other AI initiatives.
Governed with EU-aware controls
Governance is built into the lifecycle, not bolted on. Controls are gated, evidence trails accumulate, and EU regulatory weighting is applied from day one.
Documented for your team to operate
Performance, cost, and operating guidance are handed off in plain documentation — so your team runs the system without depending on us.
Production-grade, governed, and documented — every engagement, whether it's a single system or a full delivery program.
Who We Build With
Teams that know what they want built.
We ship better when clients come with clarity. Before a Solutions engagement begins, we look for three things.
01
A scoped problem
A specific business outcome, not a general desire to 'do AI.' If you're not sure yet, start with Advisory.
02
Access to the data and systems
We can build anything, but only if we can reach what we need to build with. Clear data and system access makes every project faster.
03
An operator waiting to receive it
Someone on your team who will own and operate the system after we hand it off. If there's no operator, the system shouldn't be built yet.
Systems We've Shipped
Questions
Before we build.
Start Building
Let's talk about what you need shipped.
Every Solutions engagement starts with a scoping call. Share the system you need. We'll tell you honestly what it takes to build it.