Get AI that does the work
running in production.

Internal enquiry handling, document search and summarization, automation of routine work — give it a goal and an AI agent runs the tasks autonomously. We build them from PoC through production. We connect safely to your existing systems via MCP, ensure permissions and audit, and embed the agent into real work. We don’t stop at a demo — we walk with you all the way to something that actually gets used.

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Pain points

Do any of these sound familiar?

Where AI agent projects stumble is rarely the building — it’s getting them all the way onto real work.

You built a demo, but it never got embedded into real work and went unused.

You want AI to handle internal data safely, but designing permissions and audit is hard.

You want to try generative AI, but you don’t know which task to start with to see results.

A build tied to one specific model leaves you worried you can’t keep up as things evolve.

You can’t judge how to embed AI into work given that accuracy will never be perfect.

Reasons

Our strength is getting AI onto real work, not just building demos

With generative AI, the hard part is less “building it” than “making it something used in real work.” We practice AI-driven development on our own products and bring that experience in directly.

We practice AI-driven development ourselves

We use AI coding and MCP daily in developing and operating our own SaaS, such as Kitei-Log and Keibi-Sign. We bring in experience from running these in practice — not textbook knowledge.

We don’t stop at a demo

We own the work from PoC through production. We design the points where a person should check (human-in-the-loop) to fit your workflow, and land it in a form that runs safely even when accuracy isn’t perfect.

A design not locked to a model

We avoid tight coupling to a specific LLM product, so you can swap in a better model when one arrives. In a fast-moving field, we leave room to switch.

Permissions and audit from day one

We keep the data AI can touch via MCP to a minimum and record who ran what and when in an audit log. We answer the worry of letting AI handle internal data through design.

Scope

What we cover

From designing and building the AI agent to connecting with your existing systems and running it in operation — end to end.

01 Agent

AI agent development

We build AI agents that act autonomously toward a goal — enquiry handling, document processing, automation of routine work. We design the points where a person should check (human-in-the-loop) to fit your workflow, and land it in operation that runs safely even when accuracy isn’t perfect. We ourselves use AI-driven development and MCP daily in building and operating our own SaaS, and bring that hands-on experience in directly.

Auditing workflows and scoping what to agentize
Implementing tool execution and autonomous loops
Designing human-in-the-loop checkpoints
Defining acceptance criteria (accuracy, tolerance)
Fallback and escalation on failure
Recording execution logs and reviewing

02 MCP

Connecting existing systems via MCP

We connect to your internal DBs, groupware, and SaaS via MCP (Model Context Protocol) so AI can handle them safely. Without rebuilding your existing systems, we keep the data AI can touch to a minimum and provide a connection layer that ensures permissions and audit logs. MCP is a technology we use heavily in our own daily development — because it’s standardized, we can flexibly add or swap connection targets.

Connecting to internal DBs / groupware / SaaS
Minimizing read and write permissions by design
Recording and retaining audit logs
A connection layer that doesn’t rebuild existing systems
Controlling API rate limits and timeouts
Adding and swapping connection targets

03 RAG

Internal data search (RAG)

We build a mechanism (RAG) where AI answers by referencing internal documents and knowledge. Every answer cites its supporting sources so the person in charge can verify correctness on the spot as it’s embedded into work. Document updates are applied incrementally, and reference scope is controlled by viewing permissions, reducing the worry that information that shouldn’t be shown gets mixed in.

Ingesting internal documents and knowledge
Vector search and re-ranking
Presenting sources (citations)
Applying updates (incremental indexing)
Reference control based on access permissions
Evaluating answer accuracy

04 Operation

Accuracy monitoring and operational improvement

After launch, we monitor accuracy and response time and keep up with updates to prompts, data, and models. By building in a way that isn’t locked to a specific LLM product, we keep it swappable for a better model when one arrives. We collect and review error cases, make cost and token usage visible, and connect it all to rolling out to the next task.

Monitoring accuracy and response time
Continuous improvement of prompts and data
Model swapping (a non-locked design)
Making cost and token usage visible
Collecting and reviewing error cases
Rolling out to the next task

Process

How we work

Rather than going straight to production, we first confirm “is it usable” on one task, then expand.

01Choosing the target task — we narrow to one task that shows results easily and has ground-truth data.
02PoC — we validate accuracy and fit with real data and judge against the acceptance criteria.
03Production build — we implement it including connection to existing systems, permission design, and monitoring.
04Operation and expansion — we improve while monitoring accuracy and roll out to the next task.

Plans

Engagement formats

Rather than a company-wide rollout from the start, we begin by validating one task and expand step by step. We scope the range to fit each engagement.

PoC

Validate on one task first

Choose one task that shows results easily
Validate accuracy and fit with real data
Judge whether to continue against acceptance criteria
Evaluate for real operation, not a demo

Build

Production build

MCP connection to existing systems
Permission design and audit logging
Human-in-the-loop checkpoint design
Implementation through monitoring and fallback

Operate

Operation and rollout

Monitoring accuracy and response time
Continuous improvement of prompts and data
Model swapping and cost visibility
Rolling out to the next task

Comparison

Compared with the alternatives

“Trying it out” is possible with any approach. The dividing line is whether you can get it safely onto real work and sustain it in operation.

Adopt a general AI tool

An off-the-shelf service as-is

Hard to fit to your own workflows
Existing-system connection and permission design are on you
Hard to build up and evaluate accuracy
A model change in operation means a rebuild

Build in-house

Built by your own engineers

Requires hands-on experience with AI-driven development and MCP
Audit and fallback design are heavy lifts
Hiring and ramp-up take time
Sustaining production operation tends to hinge on individuals

SHANNON

Walking with you from PoC to production

Building directly from the practice we honed on our own SaaS
Connecting safely to existing systems via MCP
Covering accuracy gaps with human-in-the-loop
Model-agnostic, sustained through accuracy monitoring

FAQ

Frequently asked questions

Yes. We start by helping you figure out which task suits an AI agent. We choose, together, a task that shows results easily and has ground-truth data, and begin with a small validation.

With an MCP connection layer, we limit the data AI can touch and ensure permissions and audit logs. We design on the premise of a contract plan for business use where input data isn’t reused for training.

Yes. We design the points where a person should check to fit your workflow and embed it so mistakes can be caught. We design to each task’s tolerance — for example, “misses are a problem, but false positives are acceptable.”

As a rule, no. We add an MCP connection layer to your existing systems so AI can handle them safely. We also handle adding AI to aging existing products and rebuilding them in phases.

Related services

AIO

AI Search Optimization (AIO / GEO)

Measures to become something generative AI cites and recommends. Beyond being “used” by AI, we design the side that gets “found” through AI, too.

See AI search optimization Team

Engineering Agency

If you want a partner for the whole product, not just AI development, we take it on as a team through design, implementation, and into operations.

See Engineering Agency PoC

PoC & Prototyping

If you’re at the “let’s try small first” stage, we validate the technical direction in a few weeks before full build. A “verify before you build” approach.

See PoC development

Get in touch

Contact

Let’s talk — the first consultation is free.

Even if your requirements aren’t fixed yet, that’s fine. We reply within 2 business days.

You can also reach us by phone (050-1794-9651, automated voice; we call back on business days). For detailed enquiries with budget and timing, please use the contact form. Note: we do not accept sales solicitations.

Get AI that does the workrunning in production.