Technology & SaaS
Ship faster, run leaner, and make AI part of the product.
Software businesses are expected to add AI capability while improving gross margin and reliability at the same time. We work on both sides of that equation — building AI into the product, and engineering the platform and operations that keep unit economics sustainable as usage grows.
Industry challenges
What we see in this sector
The operational patterns that recur across organisations in this industry.
AI expectation vs. capacity
Customers and investors expect AI capability faster than product teams can safely deliver it.
Infrastructure cost per customer
Cloud and inference cost growing faster than revenue on certain accounts.
Reliability at scale
Incident load rising as the platform and customer base grow.
Back-office lag
Finance and operations processes still manual while the product is highly automated.
AI opportunities
Where AI applies
Opportunities that follow from those challenges — chosen because the work is high volume, the inputs are varied, and the outcome can be verified.
In-product intelligence
Assistants, generation and summarisation built into the product experience with grounded, citable output.
Support deflection
Documentation-grounded answers for common questions, with clean escalation to human support.
Engineering acceleration
AI-assisted development, testing and code comprehension across the delivery lifecycle.
Usage & churn analytics
Behavioural signals surfaced to customer success before renewal conversations.
Technology solutions
The engineering underneath
- Product & platform engineering
- Cloud architecture and FinOps
- DevOps, SRE and observability
- Data platforms and product analytics
- Application and AI security
- Quality engineering and test automation
Business services
Operations and finance support
- Finance operations and revenue accounting support
- Payroll
- FP&A and investor reporting support
- Customer operations process services
Use cases
Representative engagements
Examples of the work this capability supports. These illustrate applicable use cases; they are not descriptions of delivered client projects.
- 01Grounded in-product AI assistant over product documentation and customer data
- 02Cost-per-tenant attribution across cloud and model inference spend
- 03Automated regression and evaluation suite covering AI feature output quality
- 04Consolidated usage analytics feeding renewal and expansion decisions
Transformation approach
How we would take this forward
The same delivery method across every sector — understand the business first, then sequence the work so value arrives during the programme.
Understand
We start with the business and the challenge — how work actually happens, what constrains it, and what a good outcome looks like.
Strategize
We define the right solution and sequence it, making the trade-offs between value, risk, effort and time explicit.
Design
We design the architecture, process and experience together, so the solution fits the organisation that has to run it.
Build
We implement with engineering discipline — tested, documented and built to be maintained by the people who inherit it.
Transform
We introduce AI and automation where they change the economics, and support the process and role changes that adoption requires.
Optimize
We measure performance in production and keep improving accuracy, cost, reliability and outcomes over time.
Working on a technology & saas challenge?
Tell us what you are dealing with. We will bring the right mix of AI, engineering, finance and operations expertise to the conversation.