01 / Customer question
The commercial context
Buyers and agents need accurate capabilities, not broad positioning.
Enterprise buyers use AI to compare capabilities, deployment models, integrations and licensing. A vendor can appear in the shortlist while being described incorrectly. Missing documentation or invented security claims create problems that a broad positioning statement cannot resolve.
Agents may also change how customers access software and how usage is priced. We assess buyer information separately from authorised product tasks, private customer knowledge and source code. The scope connects discoverability with practical access and commercial decisions for your product and security teams.
Customer journeys
Evaluate buyer roles and authorised agent tasks.
We agree the questions, audiences and markets to assess. The examples below show what we can investigate, with the final scope based on your priorities.
02 / Customer question
Does a comparison preserve current licensing, capabilities and verified security statements?
03 / Customer question
Can an authorised agent discover and complete an agreed task through an API or MCP interface?
01
Shortlist and claims research
We evaluate defined buyer roles and buying stages against current product, security and licensing references. The findings distinguish shortlist presence from suitability and material claim accuracy.
You receive: Shortlist baseline and material-claim gaps.
02
Documentation and integration architecture
We assess developer and integration documentation, API information and tool metadata. The priorities identify what buyers or agents need to understand and where an interface may support a defined task.
You receive: Documentation priorities and agent-access feasibility.
03
Permissions and commercial pilots
We scope authenticated tasks, tenant boundaries, action limits and usage economics with product and security owners. A pilot specifies permissions, quality criteria and the cost of successful task completion.
You receive: Agent pilot specification and commercial options.
Internal AI readiness
Prepare engineering and service teams for controlled assistance.
Engineering and service-desk assistance requires permissioned knowledge and secure workflows. We assess retrieval quality, tenant isolation, source-code boundaries, prompt-injection risks and review for impactful actions.
MCP may be an appropriate interface for some tasks, alongside existing APIs or other integration paths. It is selected against the use case, not treated as a guarantee of demand. Staff training, records and task evaluation form part of a controlled adoption plan.
A practical first engagement
Software AI Shortlist & Agent Readiness Assessment
We agree product families, buyer roles, markets, competitors, current references and candidate agent tasks. You receive shortlist and claim findings, documentation priorities, access and permission dependencies, commercial questions and a roadmap for an evaluated integration or internal-use pilot.
01
Agree the scope
We agree the business questions, reference sources, AI platforms and teams involved. Before the assessment begins, we confirm the access needed and any work it depends on.
02
Review the evidence together
We separate what the AI answer shows from the content, data or workflow issues behind it. Your teams help decide which changes matter and which are feasible.
03
Choose the next action
The plan identifies the priorities, who is responsible, what each change depends on and how it will be checked. If a pilot is worthwhile, we agree how to run and evaluate it with the teams involved.
Evaluation
Measure the changes that matter.
We agree how to evaluate results before making changes. Each answer is recorded with its question, market, AI platform and date. Repeat checks help identify patterns. We assess commercial results and improvements to internal workflows separately with your teams.
Shortlist presence
Capability claim accuracy
Qualified opportunities
Authorised task success and cost
Part of Ayima
Technical search expertise. Practical enterprise thinking.
SWLR combines AI visibility and readiness consulting with Ayima's experience in enterprise search. We investigate the content, data and technical issues behind your business question, including access permissions, then help your teams decide what to do next.
Common questions
Before we begin.
Can we influence AI-generated vendor shortlists?
You can improve the information, accessibility and references available to the systems. We evaluate defined journeys and identify practical changes, then recheck comparable questions. No provider can guarantee inclusion in every third-party shortlist.
Do we need an MCP server?
Not necessarily. We start with the task, user, authentication and existing interfaces. An API, MCP server or another route may be suitable. Protocol choice should follow the product and permission requirements.
How do we protect customer data and source code?
Public discovery work is separated from private assets. Internal and agent use cases need defined access, tenant boundaries, records and review. The pilot should test those controls rather than assuming a connected tool has appropriate authority.
Signal → Evidence → Action
Start with the questions that matter.
Discuss your products, buying journeys and the customer or agent tasks you want to support.
Discuss your AI strategy