We engineer the information behind money.

Monitoss is a product engineering studio for financial information. We design and build the data pipelines, APIs and interfaces that turn raw market, rate and account data into numbers people can trust and act on.

Focus
Fintech data infrastructure and products
Engagement
Product builds, platform teams, technical advisory
Status
Financial data engineering and AI workflow exploration

Capabilities

Four disciplines, one team. Most projects touch all of them, so we build them together instead of handing work across vendors.

  • Financial data pipelines

    Ingestion and normalization for rates, FX, equities and macro series from exchanges, central banks, aggregators and partner feeds. Every value is versioned and traceable to its source.

    • Streaming and batch ingestion with schema validation
    • Point-in-time storage for backtests and audits
    • Currency, calendar and corporate-action normalization
    • Data-quality checks with alerting on gaps and outliers

  • APIs and developer platforms

    Public and partner APIs that expose financial data cleanly, with the documentation, keys and limits a real developer program needs.

    • REST and GraphQL design with OpenAPI specifications
    • Auth, API keys, usage metering and rate limiting
    • Webhooks and real-time feeds over WebSocket
    • Developer portals, SDKs and sandbox environments

  • Money products

    Web and mobile interfaces where financial numbers have to be read correctly the first time: comparisons, calculators, dashboards and alerts.

    • Rate and product comparison engines
    • Loan, savings, investment and tax calculators
    • Portfolio and cash-flow dashboards
    • Price, rate and threshold alerting

  • Analytics and research tooling

    Internal tools for analysts and editors who publish financial insight: query layers, chart pipelines and publishing workflows tied to live data.

    • Semantic layers over warehouse data
    • Reproducible chart and report generation
    • Model-assisted summarization with source citations
    • Editorial review and publishing workflows

AI research roadmap

Exploring how language models can help people work with financial information while keeping source data, numerical calculations and human judgment clearly separated.

PRODUCT DIRECTION · EXPLORATION

Financial questions deserve traceable answers.

Our proposed direction combines structured financial data infrastructure with AI-assisted research workflows. We are evaluating Claude as a possible language-model component for interpreting questions, organizing retrieved evidence and drafting explanations. This section describes a development direction, not a released product or an existing Anthropic partnership.

01 / RETRIEVE

Source-grounded retrieval

Identify relevant records, timestamps, methodologies and source documents before producing an explanation.

02 / ANALYZE

Natural-language exploration

Explore structured market, rates and financial datasets through questions, with calculations performed by deterministic services.

03 / DRAFT

Research and reporting

Evaluate model-assisted summaries, comparative analysis and report drafts with explicit citations and reviewer approval.

04 / VERIFY

Evaluation and safeguards

Test citation accuracy, numerical fidelity, data freshness, access controls and human review before considering production use.

Existing focus

Financial data pipelines, APIs, interfaces and analytical tooling, as described in our engineering capabilities.

Exploration

Claude API feasibility, evidence retrieval, model evaluation and prototype design. No public launch date is claimed.

Future validation

Benchmarking on representative financial queries, security review, usability tests and product readiness assessment.

Product status: exploratory roadmap. References to Claude identify a technology being evaluated, not an endorsement, partnership, approved grant or currently deployed integration.

Stack

We choose boring, well-understood tools for the core and reserve new technology for the places it earns its keep.

TypeScript / Python / Go / PostgreSQL / ClickHouse / Kafka / dbt / Airflow / Next.js / React Native / Kubernetes / Terraform / AWS / GCP

Data layer

PostgreSQL for transactional state, ClickHouse for time-series analytics, Kafka for event streams, dbt and Airflow for modeled, scheduled transformations.

Application layer

TypeScript and Go services behind typed API contracts. Next.js for web, React Native for mobile, shared design tokens across both.

Infrastructure

Infrastructure as code with Terraform, containerized workloads on Kubernetes, CI/CD with automated tests, and observability from day one.

Process

A fixed sequence with a clear deliverable at each stage, so you always know what you are paying for and what comes next.

  1. Discover

    We map your data sources, regulatory constraints, users and the decisions the product needs to support.

    Output: scope, data inventory, risk list

  2. Model

    We define the data model, API contracts and system architecture before writing production code.

    Output: architecture doc, API spec, prototype

  3. Build

    Two-week iterations with working software at every review, tested against real data rather than fixtures.

    Output: production releases, test suite

  4. Operate

    We run, monitor and improve the system with you, or hand it over to your team with full documentation.

    Output: runbooks, dashboards, handover

Engineering standards

Financial information is only useful if it is correct, current and explainable. These are the rules we build to.

Every number has a source

Values carry their origin, retrieval time and transformation history, so any figure on screen can be traced back and audited.

Correctness is tested

Financial calculations are covered by property-based and reference tests, with decimal arithmetic and explicit rounding rules.

Security by default

Least-privilege access, encryption in transit and at rest, secrets management, dependency scanning and audit logging on sensitive operations.

Built to be handed over

Readable code, documented decisions and infrastructure as code. You own everything we build, and your team can run it without us.

Company

Financial information engineering, data products and exploration of AI-assisted research workflows.

Core work

Financial data pipelines, developer APIs, financial interfaces and analytics tooling.

Responsible development

Traceable source data, deterministic financial calculations, model evaluation and human review.

Legal entity information

Legal entity name, incorporation date and registered location will be published after verification against official company records.

Official contact

hello@monitoss.com — verify mailbox operation before deployment.

Have a financial data problem worth solving?

hello@monitoss.com

New projects

Tell us what you are building, the data involved and your timeline. We reply within two business days.

Data partnerships

Data providers and platforms interested in distribution or integration work are welcome to reach out.

Careers

We work with engineers who care about data correctness. Send your work and a short note to the same address.