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kinekube

Solutions — the software factory

Enterprise software, engineered and deployed at scale

A unified software factory engineered for high-complexity enterprise workloads. Bring a workflow you run today; take back an application you own.

Book an architecture callCapabilities matrix

One team, both halves

One unified architecture for complex workflows

The same clusters, the same retrieval layer, the same telemetry — scoped to your workflow and handed over as a working application rather than a capability you then have to assemble.

Natively architected for AWS and Nutanix environments. We are not learning the substrate on your budget.

DELIVERYREVIEWREASONINGEXTRACTIONINTAKEDOCUMENTSCONVERSATIONS

Capabilities matrix

Capability, build, and where it lands

One table. Each row bridges an abstract capability with a concrete build and the industries it serves — so you can find the fit without scrolling three sections.

CapabilityExample buildVerticals
Document intelligenceTouchless ordersLogistics, Healthcare
Document generationAI-powered document creationInsurance, Life sciences
Decision supportClinical recommendation engineHealthcare, Financial services
Conversational surfacesNaming governanceManufacturing, Construction
Operating platformsInvestment management platformFinancial services

What the build involves

Each row, opened up

What the work looks like in production, and what makes it production-grade. No case studies and no invented numbers.

Document intelligence

Touchless orders

Orders arrive as a fax, an email attachment or a portal upload, and somebody retypes them. This reads them instead, checks them against your catalogue, and raises the order — leaving a person only the lines it is genuinely unsure about.

Logistics · Healthcare

Typically looks like

  • Intake from mailbox, fax gateway, portal upload or SFTP drop
  • Classification, then extraction against a schema you define
  • Catalogue, price and tolerance checks before anything is raised
  • A review queue for low-confidence fields, not whole documents
  • The order written into your ERP with the source document attached

What makes it production

  • Per-field confidence, not one score for the whole document
  • An audit trail linking every value back to its region on the page
  • Accuracy measured against a held-out set that you keep

Document generation

AI-powered document creation

The documents that have to be right: reports, submissions, proposals, policy summaries. Drafted from material you have already approved, cited back to it line by line, and routed to whoever signs.

Insurance · Life sciences

Typically looks like

  • Templates and section rules taken from your existing output
  • Drafting grounded in your approved source material only
  • A citation on every claim, back to the passage it came from
  • Review and approval routing, with tracked changes preserved
  • Export into the format your regulator or your client expects

What makes it production

  • It cannot assert anything it is unable to cite
  • Every version retained — who changed what, and when
  • An evaluation suite that runs before any prompt or model change

Decision support

Clinical recommendation engine

Assembles the guideline, the history and the contraindication at the moment a decision is being made, inside the system already open. It recommends and shows its reasoning. The clinician or the adviser decides.

Healthcare · Financial services

Typically looks like

  • Context assembled from the system of record, never re-entered
  • Guidelines and protocols kept current as your corpus changes
  • The recommendation shown alongside the evidence behind it
  • Delivered inside the existing workflow, not on a second screen
  • Every suggestion logged — accepted or dismissed, and why

What makes it production

  • Advisory by design: the system never acts on its own
  • Refusal paths for when the context is thin or contradictory
  • A review loop that can change behaviour without a release

Conversational surfaces

Naming governance

Part numbers, naming conventions and classification applied while an engineer or project team is creating rather than discovered at review — so a duplicate is caught before it becomes two identifiers for one thing.

Manufacturing · Construction

Typically looks like

  • Your naming convention encoded as rules rather than a PDF
  • Validation inside the PLM, CAD or project workflow as items are created
  • Similarity search across existing records to catch near-duplicates
  • Attribute extraction from drawings and supplier datasheets
  • A governed path for exceptions, each with an owner and an expiry

What makes it production

  • Rules are versioned and testable, like any other code
  • Bulk reclassification of the back catalogue, dry-run first
  • Reporting on what is drifting, before the audit asks

Operating platforms

Investment management platform

The operations side — positions, mandates, rebalancing, reporting — and the surface your clients actually look at, where a recommendation is explained rather than attached as a PDF.

Financial services

Typically looks like

  • Portfolio, mandate and constraint data in a single model
  • Recommendations generated against the mandate, with the rationale
  • An adviser workspace and a client view of the same underlying truth
  • Statements and reporting generated rather than assembled by hand
  • Entitlements down to the household and the individual document

What makes it production

  • Every recommendation reproducible from the data that produced it
  • An immutable record of what the client was shown, and when
  • Suitability and disclosure checks as gates, not as reminders

How the factory runs

Four phases, and the first one is not code

The failure mode of this kind of project is building the process as documented rather than the process as performed. We spend the first phase making sure those are the same thing.

  1. 01

    Workflow teardown

    We sit with the people doing the work today and map it as it is actually done — including the steps that are not in the process document. Most of the eventual design is decided here.

  2. 02

    One thin slice, in production

    A single real path, end to end, in front of a small group of real users. It settles the arguments a prototype cannot, and it puts the integration risk at the start rather than the end.

  3. 03

    Build out and harden

    The remaining paths, the exception handling, the evaluation suite, the guardrails and the failure behaviour. Load tested against your traffic shape and given a runbook your on-call can follow.

  4. 04

    Hand over, or keep running it

    Your engineers take ownership with the architecture decisions written down, or we operate it against agreed service levels. Either way the code is in your repositories from the first commit.

You can pick it up

Everything we build leaves with you

A solution you cannot take somewhere else is not a solution, it is a dependency. Here is what you hold at the end.

Code

The repository, from day one

We commit into your organisation, not ours. There is no escrow arrangement to negotiate and no runtime you have to keep paying us for.

Data

Your corpus and your embeddings

Stored in your accounts, under your retention policy. Nothing is used to train anything outside the boundary you set.

Platform

The substrate underneath

The same Terraform, Kubernetes and telemetry we would build for you as a platform engagement — so the solution is not a black box bolted to the side.

Evidence

The evaluation suite

The test set, the scoring harness and the thresholds. It is what lets you change a model next year without relitigating the whole build.

Or just have us build the platform

Architecture call

Describe the workflow. We will scope the build.

Bring the process, its volume and the systems it has to touch. Thirty minutes is usually enough for us to sketch the design and tell you whether it is worth doing.

Book an architecture callSee the platform