AI Readiness & Strategy
A four-week assessment that ends in a ranked portfolio of use cases, with costs and a go or no-go on each.
- Data estate review
- Ranked use-case portfolio
- Governance and oversight framework
AI & Data Solutions
We start with what your data can honestly support, automate the handoffs that pay back inside a quarter, and staff the human review teams that keep models accountable.
Our Service Line
Workflow Automation
We remove the waiting, re-keying and chasing between steps first, because they pay back fastest.
- Process mining
- Automations wired into your systems
- Cycle-time reporting per automation
AI-Augmented Teams
Annotation, evaluation and review teams managed as a quality operation, in a secure environment.
- Annotation and labelling operations
- Model evaluation and red-teaming
- Weekly calibration and agreement scoring
Our Process
How we take AI to production
A baseline before anything changes, one use case shipped first, and extension only where the numbers hold.
What you hold, how clean it is and which use cases it can support today.
Use cases ranked by value, effort and risk, with a recommended first three.
One use case to production with measurement agreed in advance.
Proven patterns extended, with cost per transaction tracked.
Model oversight, drift monitoring and a documented human review position.
Case Study
A digital bank
A 30-person annotation and review operation supporting fraud model retraining, running for 18 months.
- 22 pts
- gain in inter-annotator agreement
- 30
- reviewers at peak
- 0.85
- agreement floor held throughout
- The Problem
- Fraud model retraining needed consistently labelled data at a volume the internal team could not sustain, and the data could not leave a controlled environment.
- The Solution
- A zero-trust annotation environment, quality before volume with gold sets and blind double-annotation, and short annotation blocks with weekly calibration.
- The Results
- A 22-point gain in inter-annotator agreement, 30 reviewers at peak, and a 0.85 agreement floor held.
Case Studies
Results we’ve delivered
AI & Data engagements — the problem, what we did and what changed.
Artificial IntelligenceThe Productivity Inversion: Why AI Can Make Software Delivery Faster—and More Expensive
Generative AI has changed software development faster than most organizations have changed their management practices. Developers can now produce boilerplate, documentation, test scaffolding, queries, interface components, and straightforward fixes in
Artificial IntelligenceAI Is Not Killing B2B SaaS — It’s Rewriting the Playbook
For months, the tech world has been echoing a bold claim: AI is killing B2B SaaS.
Artificial IntelligenceThe Role of AI in Transforming Talent Acquisition
Artificial Intelligence (AI) has rapidly transformed numerous aspects of our lives, from personalized shopping experiences to smart home devices.
Usually it reduces overtime and backlog first. We are candid about role changes and we would rather redeploy people than surprise them.
Rarely. Most value comes from applying existing models well and fixing the data and process around them.
Into environments we agree in advance, with residency and retention written into the contract. Our annotation environment allows no local storage.
Then it says no, and you have spent four weeks instead of four quarters. That has been the honest answer roughly forty percent of the time.