We lead and deliver the agreed specialist work alongside your team or chosen delivery partner. They take the solution through implementation, deployment and support, with technical decisions, evidence and knowledge shared throughout.
Decisions backed by evidence
The decisions that make delivery work.
Architecture, evidence and operational readiness shape one another. We work through the decisions with your delivery team, so technical progress stays connected to the value the initiative needs to create.
A coherent design. Built around the whole workflow.
A fair comparison. A reasoned next decision.
Conditions for operation. Known, reviewed and owned.
01Architecture+
Will the solution fit the way you work?
We assess the design against your data, systems and intended workflow, including how it will scale as usage and data volumes grow. Together with your team, we examine platform and vendor choices, weigh the trade-offs and resolve the technical constraints that affect delivery.
A technical direction your implementation team can act on, with key choices and dependencies made clear.
02Evidence+
Does it deliver value in realistic conditions?
We define a baseline and success criteria with your implementation team, then use targeted prototyping and testing to examine performance, limitations and business value. The findings show where the approach works, what needs refinement and whether further investment is justified.
Evaluation evidence for a recommendation to scale, refine or stop.
03Readiness+
What needs to be in place for real use?
We review how the solution will be evaluated, monitored, secured and supported, including cost control and operational documentation. Unresolved gaps become prioritised actions with acceptance criteria and a clear handover to the people responsible for deployment and operation.
A clear view of readiness, the gaps to close and the evidence needed for the next decision.
Revisit these decisions as the initiative evolves and new evidence emerges.
Technical leadership in practice
Solve the hard parts. Strengthen the team.
Your people know the systems and the business. We combine that context with hands-on AI expertise, agreeing the specialist work, contributors and outputs before we begin.
Expertise in practiceSpecialist knowledge, applied with your team.
Resolve the obstacle.
We investigate technical issues with your specialists and contribute the design work, targeted prototypes or testing agreed for the initiative.
Keep progress accountable.
We review work with your implementation team or partner, assess milestone evidence against agreed commitments and report technical progress. We clarify unresolved risks and the decisions they require.
Leave the team ready to continue.
Practical working sessions, documented decisions and handover help your people understand the solution, evaluate changes and continue improving it.
Specialist knowledge, where it is needed.
A difficult integration or evaluation question may call for additional expertise. We draw on the Conceptive AI network of experts to complement your specialists, with a contribution defined around that need.
Two ways to move forward
Start with the decision you need to make.
Both engagements have a defined scope. We agree the question, the people involved and the evidence needed to make the next decision.
Focused assessment · Fixed scope
AI Delivery Review.
What is holding this initiative back?
We examine the architecture, delivery risks, evaluation and production readiness of one AI initiative to identify what needs to change.
What you take forward
Prioritised actions and clear next decisions, backed by an assessment of the issues that matter to delivery.
We agree the review boundaries and the information needed to assess the initiative before starting.
We work with your implementation team to test one use case against a baseline and agreed success criteria, using targeted prototyping and technical problem-solving.
What you take forward
Evaluation evidence and a recommendation to scale, refine or stop, so you can judge the next investment.
Data access and implementation resources are confirmed before the sprint starts.
Explore the findings, test evidence and recommendations that help you decide what to do next.
AI Delivery Review
Choose the question closest to yours.
What lands on your desk
“The demo works. Why does it fail in real use?”
Executive sponsor
“Are we fixing the cause or another symptom?”
Delivery lead
“Is it the model, the data reaching it or the connection?”
Technical lead
A visible problem. A cause that needs to be clear.
Conceptive AI / Technical diagnosisIllustrative extract
Pinpoint the cause. Focus the correction.
We trace the problem across the AI, its data and connected systems. Your diagnosis separates confirmed causes from questions that still need evidence.
Our recommendation
First give the AI the right information and reliable access to your systems. Then decide whether the AI itself needs to change.
Information reaching the AI
Tests use one version of the information; everyday use supplies another. We identify where they differ and which connection needs correcting.
System connections
A connected system responds too late, but the failure is reported as an AI error. We identify the connection to fix and how the solution should respond when it is unavailable.
Evidence of reliability
The overall test score hides failures in important situations. We identify the cases to test again and what the solution must demonstrate before it is accepted.
Technical action brief prepared
A prioritised correction brief for your delivery team, with proposed owners and checks for each fix. Suspected causes that still need investigation are clearly separated from changes ready to authorise.
How your technical requirements shape the review.Diagnosis: connect the observed problem to a technical cause.+
01
Expected behaviour
Define what the solution needs to do.
Required accuracy, response time, access restrictions and the response when a connected system is unavailable give the review a concrete reference.
In your diagnosis
We show where the solution falls short of the requirement and which part of the system needs attention.
02
Available evidence
Separate a finding from a hypothesis.
Architecture records, test cases and operating logs reveal different parts of the problem. Missing evidence limits what can be concluded.
In your action brief
We distinguish supported causes from open questions and specify the investigation or confirmation needed next.
What lands on your desk
“Can we serve more demand without costs rising as quickly?”
Executive sponsor
“Are we using too much technology for routine tasks?”
Technology lead
“Which design change is worth making next?”
Product owner
A solution with potential. Technical choices that affect its economics.
Conceptive AI / Architecture optionsIllustrative extract
Better performance. A considered technical investment.
We compare model, design and platform options against required quality, response time, operating cost and expected demand, making the trade-offs clear.
Our recommendation
Match the technology to each task and reduce repeated work before paying for more capacity.
AI suited to the task
The same AI model handles simple and complex tasks. We identify where simpler tools could meet the required standard and where more capable AI is worth evaluating.
Repeated work
The solution prepares the same unchanged information for every request. We identify what could be reused or processed at the same time, with checks to keep information current and access restricted.
Platform choice
We compare improving the current platform with moving, including capacity, reliability, cost and the effort of switching. The decision record states which limits would justify a change.
Technical comparison prepared
A preferred option, alternatives and the evidence needed before committing. The comparison sets the quality, response time, cost per completed task and demand each option must support.
How your technical requirements shape the review.Improvement: weigh performance gains against cost and complexity.+
01
Service requirements
Define the improvement worth pursuing.
Quality, responsiveness and expected demand determine which design changes could matter to the initiative. A faster or cheaper option still needs to meet the required standard.
In your options assessment
We compare approaches against the same requirements and identify where complexity adds value or creates avoidable cost.
02
Technical trade-offs
Make the case for the next design choice.
Platform fit, maintainability, reliability and migration effort influence whether a promising option is worth taking further.
In your decision record
We explain the preferred direction, its assumptions and the evidence needed before committing to the change.
What lands on your desk
“Could AI take a task from request to completion?”
Executive sponsor
“How would an agent work with our existing systems?”
Technology lead
“How do we know it has completed the task correctly?”
Product owner
An ambition for AI agents. A practical route to delivery.
Conceptive AI / Agent design briefIllustrative extract
From answers to action. With a clear technical plan.
We assess how an AI agent could choose its next steps and use connected tools to complete an agreed task. The review identifies a suitable design, the connections needed and the evidence to establish whether it works.
Our recommendation
Design the agent around a complete task, with the tools to act and clear points for human approval.
A task worth delegating
The proposed task needs information from several systems, and its next step depends on what it finds. We identify the decisions an agent could make and the steps that should remain fixed.
Connected action
The design gives the agent limited access to named tools for checking records and preparing updates. Changes to live records, spending and external commitments require approval.
Proof of completion
The evaluation brief defines the result that must be present in the connected systems. It includes missing information, interrupted tasks and failed connections, with checks of both the agent’s response and the work actually completed.
Agent design brief prepared
A recommended approach, required connections, permission limits and a test plan for your delivery team. The next decision is whether to validate this design in a focused proof of value.
How your technical requirements shape the review.AI agents: connect useful autonomy to a design you can test.+
01
Task and authority
Define what the agent can complete.
The intended result, available tools and allowed actions determine how much freedom the agent needs. Some tasks benefit from choosing the next step; others can follow a fixed sequence.
In your agent design
We map the task, tool access, human approvals and stop conditions so your delivery team has an explicit design to work from.
02
Evidence of completion
Check the result and the actions taken.
Successful completion depends on the result in your systems as well as the answer the agent gives. Tests need to cover incomplete work, incorrect actions and failed connections.
In your evaluation brief
We specify checks of the finished task and action record, including permission limits, human intervention and operating cost, before broader use is considered.
Illustrative extracts, not client findings. Your review’s scope and outputs are agreed before we begin, based on the initiative and evidence available.
Proof-of-Value Sprint
See what an AI agent trial could show: what works, where the value lies and what to do next.
What lands on your desk
“Could an agent turn what we know into an advantage?”
Executive sponsor
“Can it complete the task in our systems?”
Operational lead
“Would a simpler solution do the job?”
Technology lead
Your expertise. More of it put to work.
Conceptive AI / Agent trial findingsIllustrative extract
What makes your business different. Put to work.
We test one task with your implementation team, comparing the agent with today’s way of working and a simpler AI solution.
What the trial shows
The agent puts your business knowledge to work and follows through with action. The results support a larger trial.
Your knowledge improves the resultValue demonstrated
Your records and expert guidance help the agent choose an action that fits the situation. We show where it still needs a person’s judgement.
Work gets completedTested in connected systems
We check that the agent has completed the permitted steps in your systems. With the same information, the simpler AI solution still needs a person to coordinate those steps.
The value merits a larger trialFurther testing recommended
The findings account for completed work, human effort and running costs. Harder cases and higher usage still need testing.
Your evidence pack
A clear comparison, test results and a record of the actions taken. You see what worked, what did not and what remains untested.
How we test your business advantage.Value: what does your knowledge add, and what does the agent add?+
01
Define the advantage
Choose a result that matters.
Your team’s expertise and the information your business holds shape the task. Together, we agree what success should mean for the people doing the work.
In your trial brief
We turn that goal into agreed checks for the result, human effort and cost.
02
Compare fairly
Use the same task and information.
We compare the agent with today’s process and a simpler AI solution. Checks cover completed work, answer quality and human help needed.
In your findings
We show where the agent helps, where people still step in and where a simpler solution is enough.
What lands on your desk
“What should we invest in next?”
Executive sponsor
“Will it still work when more people use it?”
Operational lead
“What do we need to know before spending more?”
Finance lead
A promising result. A clear next investment.
Conceptive AI / Agent investment recommendationIllustrative extract
Your next investment. Backed by evidence.
We turn the results into a recommendation: scale, refine or stop. Your team receives the next steps and the evidence needed to decide.
Our recommendation
Run a larger trial of the same task. Check that the benefit holds with harder cases and expected demand before committing to everyday use.
Keep what worked
Your brief identifies the knowledge, decision rules and system connections worth keeping. Your delivery team receives the test records behind that recommendation.
Test where it may struggle
Try missing information, failed connections and harder cases. Check when the agent should ask for help or stop, and the effect on quality and cost.
Agree when to go further
Expand if the benefit holds and the solution is ready to operate. Refine it where needed. Choose a simpler solution or stop if the agent no longer earns its cost.
Your next-stage brief
Your team receives the agreed prototype work, test records and technical notes, plus a plan for the next trial: scope, responsibilities and decision criteria.
How we test your business advantage.Investment: what must hold true as usage grows?+
01
Protect what worked
Carry the useful knowledge forward.
More users and harder cases can change what the agent needs to know. Reusing the prototype alone may not preserve the benefit.
In your next-stage brief
We identify the knowledge, rules and system access to retain, and the gaps to address before the next trial.
02
Make the next decision clear
Agree what would justify wider use.
Reliable results, affordable running costs and workable human oversight all matter. The next trial must show whether the agent is ready for wider use.
In your decision criteria
We define the evidence for expanding, making changes, choosing a simpler option or stopping.
Illustrative AI agent Sprint extracts, not client results. Tests and success criteria are agreed for one use case. The findings inform the next investment; wider deployment requires further evidence and preparation.
