05 /Services
AI Integration & Enablement
Choose a useful task in your product, engineering workflow or business process. We assess readiness, compare existing tools with a custom integration and pilot the smallest end-to-end approach. Evaluation and QA are built into the work, with people accountable for approvals. Your team receives the integration, evidence and operating guidance to decide whether to extend, revise or stop.
Capabilities
The specific pieces
AI Integration in Products
Assistants, search and document workflows that earn a place in your product.
AI in Software Delivery
Earlier feedback across requirements, implementation and release, with engineers accountable for the result.
AI in Business Processes
Turn repetitive company work into a controlled pilot your team can adopt and measure.
Scope
What’s included
Useful features in your product
Customer support assistants
Answers grounded in your approved help content, with source references, clear uncertainty handling and a route to a person when the assistant cannot resolve the request.
Smart search and knowledge retrieval
Search across product documentation or internal knowledge, respecting the same access boundaries as the source system. We check retrieval separately from the answer it produces.
Document processing and personalisation
Extraction and classification checked against business rules, plus relevant recommendations where approved inputs support them. Validate the resulting task, with an ordinary search or rules-based fallback when generation adds no value.
AI across delivery and company work
Software delivery and custom bots
Requirements refinement, development assistance, PR/MR review, ticket coverage checks and test generation integrated with existing repositories and CI. Bots propose reviewable work; engineers and QA retain approval, independent expectations and release ownership.
Business-process pilots
Identify valuable tasks, assess source readiness and pilot document intake, knowledge work or controlled workflow automation. Existing tools, exception handling and action permissions are part of the design.
Training, adoption and measured impact
Train the people using and reviewing the system. Compare complete task effort, correction time, quality and cost with a recorded baseline. Govern new data sources and actions rather than extrapolating a narrow pilot into an unrestricted rollout.
Evaluation and operation
Accuracy and failure testing
Representative examples, expected answers and acceptance criteria agreed before implementation. The evaluation includes unsupported questions, misleading inputs and failed dependencies, rather than only a successful demo.
Privacy and access controls
A documented data flow covering permitted inputs, provider handling, retention, logging and user permissions. Sensitive records are excluded or minimised unless their use is explicitly agreed.
Running costs and response time
A usage-based estimate covering model calls, retrieval, hosting and maintenance, with measured prototype latency and limits for retries and request volume. Provider charges remain visible and separate from delivery fees.
Approach
How the engagement runs
- 01
Choose one useful task
Identify the user journey, its current effort and what a successful result would look like. Check that the data and integration access are available before estimating the work.
- 02
Prototype and evaluate
Build the smallest end-to-end version, run the agreed examples and record accuracy, response time and likely usage costs. Review the limitations with your team.
- 03
Integrate and verify
Connect the approved approach to your application, permissions and monitoring. Test failure paths and the human fallback before release.
- 04
Handover and maintain
Provide the evaluation set, deployment configuration and operating guidance. Agree who reviews changes to models, knowledge sources and application behaviour after launch.
What you get out of it
How the work supports your team
A working feature evaluated against examples from the task your users actually need to complete.
A documented decision on whether to extend the prototype, change the approach or stop.
An integration your team owns, with a cost model, fallback behaviour and an evaluation set it can run again.
Deliverables
What lands in your hands
- 01
A scoped use case, acceptance criteria and approved data-flow plan
- 02
A working prototype with a repeatable evaluation set and findings
- 03
Product integration and release checks when included in the agreed scope
- 04
A usage-based operating cost estimate, configuration and fallback guidance
- 05
Source code, documentation, handover and a support window agreed in writing
08 /FAQ
AI integration questions
Does this include AI tools and bots for our engineering team?
Yes. The SDLC capability covers requirements, development assistance, code review, QA and test generation, including custom event-driven bots where needed. Product AI and company-process adoption have distinct scopes. We choose the task and evaluate the complete workflow, with human approval and independent checks.
How long does an AI prototype take, and what does it cost?
We estimate the first prototype after reviewing the task, sample data and integration access. The proposal states a target review date, included use cases, acceptance criteria and price. A prototype demonstrates feasibility; production integration, monitoring and ongoing evaluation are separately identified so a demo is not priced as a finished product.
Can you guarantee correct answers?
No universal accuracy claim would be useful. We agree how correctness is assessed for your task, evaluate representative and difficult examples, and show the results and limitations. Features that cannot meet the agreed threshold are revised or kept out of production. Source references, abstention and human escalation are designed into the appropriate journeys.
Who pays for model usage and maintains the feature?
Provider usage and infrastructure costs are shown separately from the implementation fee, with assumptions about request volume and input size. Accounts and configuration are handed over to you. The proposal names the included support period; continuing monitoring, evaluations and updates can be scoped as maintenance or handled by your team.
AI decisions and operating costs
- AI adoption & engineering
Claude vs ChatGPT: choose by task, product and evaluation
A dated buyer comparison for code review, test generation, document analysis and internal assistants. Separate chat products, APIs and coding tools before comparing results.
- AI adoption & engineering
Build or buy AI: compare the whole operating workflow
Choose an existing tool, a custom integration or a non-AI alternative using task fit, data boundaries, ownership and total operating effort.
- AI adoption & engineering
Estimate AI feature costs and ROI without a fictional multiplier
Separate implementation, usage, review and maintenance. A worked hypothetical estimate shows how adoption and correction time change the business case.
Practical companion resources
Contact
Choose your first useful AI task
Bring the task, the data sources and the tools your team uses. We will outline a controlled pilot, its evaluation and the work needed to integrate and maintain it.