Custom AI Software for
Complex Operations.

We build production systems for work that sits close to revenue, risk, and sensitive data. Less manual work, lower cost, faster decisions.

Full IP OwnershipRuns in Your CloudPrivate Data by DesignBuilt for Regulated WorkAuditable AI Decisions

How We Work

Map the Workflow.
Build the System.
Own the Result.

Manual operations accumulate spreadsheets, copy-and-paste, disconnected tools, and fragile workarounds. We start with the real process, then replace the waste with software designed to run reliably.

You work directly with senior engineers. We map the bottleneck, define what a correct result looks like, build the automation, and deploy it inside your environment.

Direct Senior Engineering

No account-management layer or junior rotation. You work directly with the engineers designing and building the system.

Workflows Turned Into Software

We turn the real steps, rules, exceptions, and approvals in your operation into software your team can run and maintain.

Your Code, Your Infrastructure

The code, deployment configuration, and documentation are handed over to your team. No proprietary platform lock-in.

What We Build

Capture More Opportunities.
Reduce Operating Cost.
Protect Revenue.

We build systems that help teams capture more opportunities, reduce operating cost, and protect revenue through faster, more reliable execution.

// Revenue & Workflow Automation

Qualify inbound demand, score opportunities, prepare responses, and remove repetitive admin from the work closest to revenue.

// Data & Decision Systems

Turn documents, images, and live events into structured, validated actions your team can inspect and control.

Reliability in Production

Schema Validation & Accuracy99.8%
Pipeline State ReconciliationSub-50ms
Deterministic Guardrail Match100%

Reliability & Evaluation

AI You Can Trace and Test.

Every automated decision is checked against defined rules, structured outputs, and logged fallbacks. Errors are caught before they reach operations.

> Initialising Applied AI Pipeline Evaluation Harness...

> Ingesting unstructured payload [Document ID: TEN-2026-X8]... RECEIVED

> Executing Stage 1: Fast LLM extraction & JSON schema binding... PASSED

> Executing Stage 2: Opportunity scoring & XGBoost parameter validation... SCORED [0.94]

> Verifying anti-hallucination guardrails & policy constraint checks... VERIFIED

> State reconciled over WebSockets. Draft proposal generated & queued for human review.

Proof of Execution

Working Systems & Measured Outcomes

Built where the stakes are real: revenue, live financial signals, and sensitive personal data. No black boxes. The architecture, controls, and results stay visible, maintainable, and owned by your team.

01

Procurement Document Automation & Bid Triage

From unstructured tender PDFs to scored opportunities and structured draft responses

The Problem

Incoming tender notices required repetitive manual reading, data entry, and judgement before the team could decide whether to bid.

The System

A two-stage AI pipeline extracts contractual variables into strict schemas, scores each opportunity against defined criteria, and produces a structured draft for review.

Outcome: Manual administration reduced by 90%, with validated outputs and a complete decision trail.

Where Else This Applies

Automating inbound sales qualification, RFP and opportunity triage, account research, proposal preparation, and targeted outbound workflows.

PythonAsync FrameworksLLM SchemasNext.js
90% Less Manual Admin
Validated Structured Output
Automated Draft Generation
02

High-Volume Financial Signal Filtering

From thousands of noisy daily candidates to roughly 30 high-quality signals

The Problem

Thousands of signals arrived each day from fast-moving, non-deterministic market data. Manual review could not keep pace, while fixed rules alone could not reliably separate useful opportunities from noise.

The System

A real-time pipeline engineers features from live market events, then uses XGBoost to score and rank each candidate before threshold and state checks produce a focused, actionable stream.

Outcome: Thousands of daily candidates reduced to roughly 30 actionable signals, achieving an approximately 50% observed hit rate in live use.

Where Else This Applies

Prioritising high-volume fraud alerts, risk cases, maintenance signals, quality failures, leads, or any event stream where teams need the few items worth acting on.

TypeScriptAWSXGBoostWebSockets
1,000s Scored Daily
~30 Signals Selected
~50% Observed Hit Rate
03

Secure Visual Analysis & Structured Reporting

Image models turned unstructured uploads into consistent reports inside a complete membership platform

The Problem

Sensitive member data and user-submitted images had to be managed securely. Sending images directly to a general model produced variable, free-form output that could not support a dependable reporting workflow.

The System

A secure membership platform controls access to user data and routes images through vision models. Outputs are constrained to defined schemas, validated, stored in Postgres, and presented as consistent reports.

Outcome: Unstructured visual inputs converted into repeatable reports, with secure user access, controlled AI outputs, and reliable backend state throughout.

Where Else This Applies

Property-condition assessments, insurance and warranty claims, maintenance records, compliance inspections, and consistent visual evidence for damage or liability disputes.

Next.jsPostgresVision ModelsSchema Validation
Schema-Validated Reports
Secure Member Data
Controlled AI Outputs

How Delivery Works

From Bottleneck to Working System.

A direct path from understanding the work to deploying software your team owns.

01

Map the Workflow

Understand the manual steps, decisions, exceptions, data, and approvals slowing the operation down.

02

Build & Test

Create the system in short stages, with agreed examples and checks defining what a correct result looks like.

03

Deploy & Harden

Run it inside your environment with monitoring, fallbacks, security, and human review where needed.

04

Transfer Ownership

Hand over the code, deployment configuration, and documentation to your internal team.

Production Standard

Built to Run, Fail Safely, and Be Owned.

The model is one component. The surrounding system makes it dependable enough for real operational work.

01

Controlled AI Outputs

Schemas, validation, evaluations, and fallback routes constrain model behaviour before results reach operations.

02

Reliable Data & State

Event-driven processing, reconciliation, and durable storage keep workflows accurate as volume and complexity increase.

03

Security Boundaries

Protected data flows, least-privilege access, and human approval are applied wherever the risk demands them.

04

Deployable & Transferable

Code, infrastructure configuration, and operating documentation ship into the environment your team controls.

Typical stack: Python · TypeScript · React · Postgres · AWS · WebSockets · XGBoost · Containers

Direct Delivery

Direct Senior Engineering. No Agency Layers.

Work directly with the people designing and building the system, from the first workflow review through deployment.

Engagement Model

Fixed-Scope Builds & Embedded Engineering

Ready to Build

Turn Manual Work Into Reliable Software.

Bring us the document process, data bottleneck, or operational workflow your team should no longer be handling by hand.