Rikitech Technology
Data and engineering for AI

The right foundation to put AI into production.

Rikitech prepares the data and engineering so AI agents operate with context, permissions and control. From diagnosis to production, with defined scope and acceptance criteria.

Talk to a specialist View solutions
AI-ready data Engineering prepared for agents Harness with governance
01 Data 02 Engineering 03 Harness Production From the data foundation to the agent in production with defined scope and acceptance criteria
AWSAzureGoogle CloudDatabricksSnowflakeMicrosoft FabricKafkaKubernetesGitHubAmazon BedrockOpenAIClaude
Our expertise

Evolve the foundation that supports your AI.

Agents deliver results when data is reliable and engineering is ready to receive them. These are the areas where we work.

Data platforms

Ingestion, ETL/ELT, batch and streaming, with curated data ready for analytics and AI.

Learn more →

Engineering prepared for AI

Code, APIs, tests and pipelines organized so agents and teams can work safely.

Learn more →

Agent harness

Context, tools via MCP, permissions, evaluations and auditing so agents operate under control.

Learn more →

Cloud and integration

Cloud architecture, infrastructure as code, APIs and events to connect systems, services and partners.

Learn more →

Security and governance

Identity, access, data protection and auditing to support compliance requirements.

Learn more →
Solutions for AI

From data to the agent in production. Tailored to your operation.

Systems APIs Events Data platform Analytics AI and RAG Batch and streaming, with role-based access control
Data foundation

Reliable data, in the format and at the time AI needs it.

We structure the ingestion, processing and access to corporate data: batch and streaming pipelines, a data platform and semantic search (RAG) over the company's data sources.

  • Databricks, Microsoft Fabric and Snowflake, with ingestion and pipelines on AWS Glue, Kinesis and Kafka
  • Curated data, ready for consumption, with role-based access
  • A foundation ready for analytics, RAG and agents
Talk about data →
Engineering for AI and harness

Agents speed up delivery when the environment is ready for them.

We prepare repositories, APIs, tests and pipelines for working with agents and build the harness that controls them: context, tools via MCP, permissions, evaluations and an audit trail. Human review at critical points.

  • Standards, documentation and tests that give agents context and boundaries
  • Harness with permissions, evaluations and observability
  • GitHub Copilot, Claude and Devin inside the delivery cycle, with CI/CD and security controls
Talk about harness →
Harness Context Tools (MCP) Permissions Evaluations Agent Audit trail and human review at critical points
Technology stack

Data first. Mature technologies, chosen for the problem.

We work with the market's leading platforms and choose the stack based on project requirements, not team preference.

Featured

Data platforms

The foundation of any AI project. We work across the three leading market platforms and choose the one that best fits your environment.

Databricks
Lakehouse, data engineering and machine learning
Microsoft Fabric
Unified data and analytics platform
Snowflake
Cloud data warehouse and data sharing
Ingestion and streaming
AWS GlueKinesisKafka
Storage and databases
Amazon S3PostgreSQLSQL Server
Development
Node.jsC# / .NETJavaGoPythonGitHub Copilot
Web and mobile
AngularReactReact NativeFlutter
Integration
API GatewayOpenAPIKafkaAmazon SQSAmazon SNS
Cloud and delivery
AWSAzureGoogle CloudLambdaKubernetesGitHub Actions
Applied AI
Amazon BedrockOpenAIClaudeDevinRAGSemantic searchAgentsMCP

Brands and logos belong to their respective owners.

How we deliver

Scope, responsibilities and acceptance criteria defined before we start.

01
Diagnosis and architecture

Objectives, systems involved and technical requirements.

02
Scope and governance

Deliverables, responsibilities and acceptance criteria.

03
Integrated implementation

Coordinated specialties, testing, and quality and security controls.

04
Deployment and evolution

Go-live, observability and knowledge transfer.

Success stories

Operational knowledge within reach of the teams.

AI knowledge hub for a large organization: operational data organized, AI integrated into the teams' work, and governance over sources and access.

Before
  • Knowledge scattered across documents and people
  • Answers dependent on whoever knows the subject
  • AI without clear rules on sources and access
After
  • AI knowledge hub, consulted by the teams
  • Answers based on the operation's own sources
  • Governance over sources and access by design

Other case studies are presented in meetings, under a confidentiality agreement.

About us

Senior engineering. Agents under control. Verifiable delivery.

Rikitech is a technology holding company working in software engineering, strategic partnerships and new business, and technology and business consulting. Our experience comes from the financial market, in highly regulated environments (Brazilian regulators and frameworks such as CVM, BACEN, COAF, B3, PLD-FT and LGPD), and extends to healthcare, services and payments.

3
business pillars
5
specialties
4
delivery stages
Talk to our team

Start with the foundation of your AI.

A concrete opportunity, an engineering scope and an execution plan. In the first conversation we map objectives, systems and technical requirements; then we send a proposal with scope and acceptance criteria.

Send emailChat on WhatsApp
Email
WhatsApp
Chat on WhatsApp