Modular intelligence for the enterprise

Engineer intelligence in modules.

EXAMPLE BUILD

Cyberbricks designs, builds and runs AI agents, and the data and cloud platforms beneath them, as interchangeable, tested modules that slot into the systems you already run.

06 capabilities06 blueprint modules10 sectors04 products
6 blueprint modules 1 slot reserved for yours
SAMPLE PIPELINE · IN EVERY RUN DEMO RUN #4810 · AWAITING PROMPT
PROMPT IN

user · operations lead
MODEL OUT

…

waiting for pipeline
The mesh

One mesh. Ten sectors. Every module in sync.

Every module we design reports into one shared control layer, so an agent in aviation and one in banking would run on the same tests, guardrails and telemetry.

Modules Shared control layer Drag the globe to spin it
Approach

AI pilots tend to stall at integration. Ours start there.

We treat every AI capability as a component with defined inputs, outputs, tests and controls. That makes it predictable to build, safe to run and easy to reuse in the next use case.

A · ADVISE

Pick the right bets

Strategy and architecture that rank AI opportunities by value, feasibility and risk, before a line of code is written.

B · ASSEMBLE

Build into your stack

Engineers who wire agents, data pipelines and applications into the platforms, permissions and workflows you already depend on.

C · OPERATE

Keep it earning

Monitoring, tuning, cost tracking and controls once modules are live, so value holds after launch day.

Our operating formulaSNAP IN EACH INPUT
Still missing: Agents, Governance. Capability stays partial until all four are in.→ CAPABILITY 50%
Capabilities

Six capabilities. Mix them like bricks.

CAP · 01

Agent engineering

Agents that use your tools and APIs to finish real work — planned, tested and observable, with people approving anything consequential.

Multi-agent designTool integrationVoice & chat
CAP · 02

Systems architecture

Target-state designs for AI-ready platforms, and realistic routes off legacy.

CAP · 03

Product engineering

From prototype to production apps with AI at the core: design, build, launch.

CAP · 04

Data engineering

Pipelines, lakehouses and semantic layers agents can rely on.

CAP · 05

Cloud & runtime

AWS, Azure and GCP foundations, GPU capacity and spend you can see.

CAP · 06

AI strategy & roadmaps

Opportunity portfolios ranked by value and feasibility, with a plan to deliver.

Blueprints

Six modules. Known ports. No surprises.

Each blueprint is a reusable module with documented inputs and outputs. Choose one to see what it plugs into and what it puts out.

Answers with sources. Actions with sign-off.BX-01 · MODULE SPEC
PLUGS INTOPolicies & SOPsCase historyUser role
BX-01AssistExpert assistant
PUTS OUTCited answersDrafted actionsApproval requests
FITS WHENSpecialists lose hours to lookups: underwriting, claims review, clinical operations, service desks.
Sandbox

Choose a sector. Send a case through.

A simulated case moves through four stations (Sense, Solve, Speak, Settle), the shape we design agent workflows around.

Aviation · SIMULATED CASE 3107Irregular-ops rebooker
01
SenseSchedule change detected on a delayed departure
02
SolveSeat options ranked by connection and fare rules
03
SpeakTravelers messaged with one-tap rebooking
04
SettleOutcome logged; edge cases sent to staff
00:01.40sSENSESchedule change detected on a delayed departure
BrickYard · product framework

Have an idea? We’ll stack it into a product.

BrickYard is how we turn one use case into a shippable product: scope it, select modules, snap them to your systems, stress-test, then ship.

S1ScopeDefine the job, the users and what success looks like.
S2SelectChoose the blueprint modules that fit.
S3SnapConnect modules to your systems and data.
S4Stress-testRun evaluations, red-teaming and a live pilot.
S5ShipLaunch, monitor and scale.
OR TRY
MANIFEST · EMPTY

Your module manifest stacks up here.

Products

Our own modules, packaged.

Products we are building from the same blueprints we use in our engineering work. Each one shows its current stage.

IN DEVELOPMENT

Callframe

Voice agents for contact centers.

Answers, resolves and transfers calls with the full context attached.

IN DEVELOPMENT

Paperlift

Documents in, clean data out.

Reads forms, contracts and records, then pushes structured data downstream.

PILOT

Meterline

See what AI really costs.

Tracks model and GPU spend by team and product, with chargeback built in.

CONCEPT

Gatekeep

One control plane for agents.

Policies, approvals and audit logs for agents from any vendor, in one place.

Engagement path

Survey. Blueprint. Build. Run.

Four stages, each with something tangible at the end. Stop after any of them and you keep the deliverables from the stages you paid for.

SurveyWEEKS 1–2

Workshops, system walk-throughs and a ranked list of opportunities.

YOU KEEPOpportunity mapReadiness scoreRisk register
BlueprintWEEKS 3–4

Module selection, integration design and a costed plan.

YOU KEEPReference architectureModule bill of materialsDelivery plan
Build2-WEEK SPRINTS

Our engineers inside your team, shipping a working increment every sprint.

YOU KEEPWorking modulesTest suitesRunbooks
RunONGOING

Monitoring, tuning and controls once modules are live.

YOU KEEPQuality dashboardsSpend trackingQuarterly reviews
Start with a Survey
Impact estimator

Size the time a module could free up.

40%HANDLED
HOURS FREED / MONTH
1,600
HOURS FREED / YEAR
19,200
≈ 10.7 full-time equivalents

Illustrative only · 1,800 working hours per role per year

Built-in controls

Every module ships with its own guardrails.

Controls aren’t a phase at the end. They’re part of the module spec, checked on every release.

Checking control 1 of 6…
Human sign-off on consequential actions
Sensitive data masked before any model sees it
Every prompt, tool call and output logged
Release tests block regressions
Spend caps per agent and per team
Controls can be mapped to NIST AI RMF and EU AI Act requirements
Start a build

Your slot in the wall is open.

Tell us what’s in the way. Someone from our engineering team reads every request and replies with a first read on feasibility, the modules that fit and a sensible place to start.

What’s slowing you down? (optional)

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