Applied AI Engineering
Applied AI Engineering

Make AI part of how your business runs.

We identify where AI can reduce manual work or create new capabilities, then build the agents, software, and integrations that make it work in production.

Talk to an AI engineer →Explore what we build
From architecture to production. Source code, documentation, and support included.
LIVE_SYSTEM
● RUNNING

  1. NODE_01

    Business data

    CRM · EMAIL · DOCS

  2. NODE_02

    Context

    KNOWLEDGE · RULES

  3. NODE_03

    AI reasoning

    MODEL + GUARDRAILS

  4. NODE_04

    Tools

    PERMISSIONED

  5. NODE_05

    Verified action

    ● VERIFIED

The implementation gap

Access to AI is no longer the problem. Implementation is.

Prototypes can be assembled in days. The difficult work begins when AI needs to understand your operation, access the right systems, respect permissions, produce a verifiable result, and keep working after launch.

OPERATIONSAPPLICATIONVERIFICATIONGUARDRAILSTOOLSCONTEXT

The model is one component. We engineer the context, tools, guardrails, software, and operations around it.

Context
The knowledge, process, and data required for the task.
Tools
Secure access to CRM, email, databases, documents, and internal software.
Guardrails
Permissions, validation rules, and human escalation.
Verification
Evidence, tests, logs, and measurable operating criteria.
Application
The agent, workflow, interface, or product people actually use.
Operations
Deployment, monitoring, support, and improvement.
How we engage

From technical definition through production and operation.

Three stages of one continuous relationship — not unrelated engagements.

  1. 01/

    AI Assessment & Architecture

    We map the operation, evaluate data and systems, prioritize opportunities, and define the architecture and execution plan.

    Client receives

    Operational diagnosis · Prioritized roadmap · Product scope · System architecture

  2. 02/

    AI Systems & Products

    We build the agents, applications, integrations, and infrastructure required for the system to operate inside the business.

    Client receives

    Production system · Integrations · Source code · Documentation · Training

  3. 03/

    Continuous AI Engineering

    We support and improve live systems as models, processes, and business requirements change.

    Possible scope

    Monitoring · Quality evaluation · New capabilities · Technical escalation

    [SLA — DEFINITION REQUIRED]

Systems, not demos

AI systems built around real operations.

  • 01/

    AI agents

    Agents that complete operational tasks across CRM, email, databases, documents, and internal systems.

  • 02/

    AI-native software

    Document analysis, contract review, scoring, reporting, and specialized products built around proprietary information.

  • 03/

    Conversational systems

    WhatsApp agents that qualify leads, schedule appointments, update systems, and escalate to people.

  • 04/

    Knowledge systems

    Operational knowledge that gives teams and agents the right context for each task.

  • 05/

    Integrations & automation

    Connected workflows across CRM, email, billing, documents, and operational software.

  • 06/

    Custom software

    Client portals, internal platforms, APIs, administrative tools, and AI-enabled MVPs.

Built to operate

An AI system is still a system. It has to work in production.

YOUR SYSTEMAI CORE
Engineering standard
  • Architecture before implementation.
  • Integration with real systems.
  • Security and permissions.
  • Verification with real information.
  • Human escalation where judgment is required.
  • Observability and support after launch.
  • Source code and documentation for the client.
The Seika method
  1. 01/● RUNNING

    Discover

    Understand the operation, systems, information, constraints, and business outcome.

  2. 02/

    Architect

    Define what AI should do, what software surrounds it, and how results will be verified.

  3. 03/

    Build

    Develop and integrate the system. Test it with real workflows and information.

  4. 04/

    Operate

    Monitor, support, and improve the system as the business changes.

[DELIVERY RANGE — POLICY REQUIRED]

Engineered in the real world

Systems designed around how the business actually works.

  • 01/

    Minapp · Mining & compliance

    Regulatory requirements read and tracked by a system, not by hand.

    Mining title holders track official requirements spread across government systems and long PDFs. Seika built the vertical platform that syncs title data, analyses the documents with AI, and turns them into obligations, deadlines, states, and alerts.

    Production platform with government integration, human approval, and multi-company operation.

  • 02/

    Equiifinanzas · Accounting & finance ops

    Scattered financial documents turned into structured, filed information.

    Accounting firms receive invoices, statements, and supporting files from every company they serve, across email, chat, and loose folders. Seika built the multi-company platform that extracts metadata with AI, classifies every document, and files it in the right Drive folder.

    Deployed platform: web portal, REST API, async processing, and Google Drive integration.

  • 03/

    Konempleo · Recruiting

    Talent search by conversation, over profiles a human already approved.

    Recruiting teams sit on thousands of CVs that keyword search barely reaches. Seika built the platform that extracts and structures each CV with AI, keeps human review before a profile becomes visible, and lets recruiters search in natural language.

    Multi-portal product with AI extraction, vector embeddings, and a conversational search agent.

Next step

Bring us the process, prototype, or diagnosis.

We will evaluate what it takes to turn it into a production AI system and define the right next step.

Talk to an AI engineer →

Henry Bravo · CEO, Seika AI · henry@seika.ai