TinyCubeWay

Nourishing agents in production

Real-time context for agents that can’t afford to be wrong.

An agent that reads stale inventory, limits or order status takes the wrong action, and it does so confidently. We build and run the context layer that keeps every agent reading live, correct business state, straight from your source systems as it changes.

Book a 30-minute review Read the engineering notes

Change events from many systems, arriving at one context store within seconds.

For fast-moving data, a nightly copy is wrong for most of the day.

An agent acts on whatever copy of the data it is given. Move along the day to compare what each copy would have told it.

0408012000:0003:0006:0009:0012:0015:0018:0021:0024:00Units in stockThe batch copy is wrong hereNightly export, 02:00Sold out, 11:42Restocked, 16:0011:45
At11:45one product, one day
Actually in stock0the truth in the orders database
Streaming context shows0a few seconds old
Nightly batch shows929 h 45 min old, off by 92 units
Illustration of one product's stock over a single day, not client data.

Trace an agent's decision back to its data.

Every answer an agent gives passes through four layers. We design and build each one, and the joins between them, to the standard an architecture review expects.

Reference architecture for agent contextSource systems feed a streaming platform through change data capture with data contracts. A real-time context layer processes events, serves them from a context store and search index, and exposes them to agents through tools that return each value with its age. A lakehouse keeps history and replay, and a semantic layer defines metrics and access for agents and dashboards. Freshness, lineage, quality and access control run across every layer.SourcesOperational databasesPostgres, MySQL, OracleSaaS and ERPCRM, billing, financeApps and devicesclickstream, telemetryStreaming platformCaptureCDC and connectorsContractsschema registryEvent logreplicated,multi-zoneReal-time context layerProcessjoins, dedupe,late dataServecontext storeIndexsearch, vectorsToolsMCP, APIsevery valuewith its ageand sourcefreshConsumersAI agentsapprove, price, promiseDashboards and appspeople, same numbersLakehouseraw, cleaned and modelled tableson open table formats; history,backfill and replaySemantic layermetrics and entities defined once, as code;approved joins; row and column accessrules shared by agents and dashboardsrevenueAcross every layerfreshness targets per entity, lineage, data quality checks, access control and audit, infrastructure as code

Real-time context layer

Agents act on what is true now, and know how old every fact is.

Typical stack Debezium, Apache Flink, Redis, Postgres, MCP

What good looks like

  • Change data capture with ordering and replay guarantees, instead of polling
  • Stateful stream processing that handles late, duplicate and out-of-order events
  • Serving stores sized for agent latency, with a freshness target for each entity
  • Every value returned with its age and source, so an agent can refuse stale data
  • Access rules enforced at the tool boundary, not left to the prompt
  • Point-in-time replay, so you can audit exactly what an agent saw when it acted

Semantic layer

One definition of revenue, customer and order, used by people and agents alike.

Typical stack Apache Iceberg, Trino, dbt, Spark

What good looks like

  • Metrics and entities defined once, as version-controlled code
  • Approved joins and grain rules, so no query can double count
  • Row- and column-level access rules inherited by every consumer
  • One query interface shared by dashboards and agents
  • Tests on every definition, run before anything ships
  • Query data where it lives when copying it is not worth the cost

Streaming platform

An event backbone that stays up, stays affordable and keeps its delivery promises.

Typical stack Apache Kafka, Amazon MSK, Redpanda, Kafka Connect

What good looks like

  • Multi-zone clusters with tested failover and disaster recovery
  • Schema registry and data contracts at every producer
  • Idempotent or exactly-once delivery wherever money or stock is involved
  • Retention, tiered storage and quotas sized against real cost
  • Lag and freshness alerts, each with a runbook behind it
  • Infrastructure as code, with rolling upgrades and no downtime

Lakehouse and ERP data

The history underneath: cheap to run, easy to replay and trusted by both people and agents.

Typical stack Databricks, Delta Lake, Apache Iceberg, Spark, BigQuery

What good looks like

  • Raw, cleaned and modelled layers on open table formats
  • Data quality checks and contracts between every layer
  • Backfill and replay paths that can rebuild the real-time layer
  • ERP extraction that respects source-system load and keeps change history
  • Cost governance across compute sizing, storage tiers and job tuning
  • Orchestrated pipelines with CI/CD, replacing scheduled scripts

How an engagement runs

Every engagement starts small and measurable. You see what we find before you commit to a build.

  1. Context-layer review

    30 minutes, free

    We look at what data your AI can and cannot see, how old it is when the AI acts on it, and where the pipeline is most likely to break first.

  2. Freshness audit

    Two weeks, fixed scope

    We measure data age at every hop against a fixed list of failure modes and hand you a written plan with priorities and effort.

  3. Build

    Short milestones

    Streaming, context-layer or semantic-layer work, delivered by the engineers who scoped it, with a working result at each milestone.

  4. Handover

    Your team owns it

    Runbooks, dashboards and a walkthrough, so your engineers can run and change the system without us.

Engineering notes

How we approach the problems we get asked about most: running streaming platforms, keeping agent context fresh, and semantic layers that give the same answer every time.

All engineering notes and research

We are deliberately small. The people who scope your project are the people who build it.

There is no account-management layer between you and the engineering, and no bench we need to keep busy. That is why we take on work larger firms won't quote: the two-week fixed-scope job, the pipeline that has been quietly broken for a year.

Registered in 2023 as TinyCubeWay IT Solutions Private Limited, Mumbai.

  • Anuj Rathi

    Head of Delivery

    Architect for AI and data systems. Leads the technical design and delivery of every engagement, from the first review to handover.

  • Ankit Mishra

    Engagement Lead

    Your first point of contact. Owns scope, timelines and commercials, and keeps the work tied to the business outcome you hired us for.

Start with a 30‑minute review

Bring one AI feature or one pipeline. We will tell you honestly whether the problem is data freshness, data definitions, or something else.

  • What data your AI can and cannot see today
  • How old that data is when the AI acts on it
  • Where the pipeline is most likely to break first
Pick a time
Email
connect@tinycubeway.com
Phone
+91 80765 10352
Office
Mumbai, India
LinkedIn
TinyCubeWay

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