Turn institutional knowledge into better services for students, staff and employers
Universities, colleges, academy trusts and skills organisations hold vast knowledge across central teams, faculties and systems. We help you organise it, bring AI into professional services with care and build the data that shows what your programmes achieve.
The problems
Where institutions lose time and insight
Fragmented institutional knowledge
Policies, processes and guidance sit in different places for each faculty, school or campus.
Professional-services productivity
Admissions, registry, finance and HR teams answer the same questions and rekey the same data.
Student and staff services
Students and staff wait for answers that already exist somewhere in the institution.
No settled data strategy
Data is collected for returns and compliance, but rarely joined up to answer the questions leaders ask.
AI adoption without a plan
Staff and students already use AI tools while policy, guidance and safe alternatives lag behind.
Reporting that takes weeks
Statutory and internal reporting is assembled by hand from several systems.
Central versus local operating models
Faculties and central teams run things differently, and the systems reflect it.
Research and evidence use
Research outputs and evidence are hard to find and reuse across the institution.
Employer and skills intelligence
Programmes need to reflect what employers want, but the evidence is scattered.
Programme impact
Showing what a programme achieves for learners and employers needs data that is rarely joined up.
Forcing events
When it usually comes to a head
- Financial pressure that makes professional-services productivity urgent
- A new student system, ERP or data platform
- Staff and student use of AI moving faster than policy
- A merger of institutions or trusts
- Funders or regulators asking for evidence of impact
What you already hold
The knowledge and data involved
- Policies, regulations and procedures, central and local
- Student records and programme data
- Curriculum documents and programme specifications
- Labour-market and employer data
- Research outputs and evidence
- Service enquiries and case records
Workflows
Workflows we change
Specific pieces of recurring work, each with an owner, a baseline and a measure of what changed.
Student and staff enquiries
Answers from approved policy and guidance, cited, for service teams to use or publish.
Policy and process search
One place to find the current policy and how it applies locally.
Reporting and returns
Reporting built on joined-up data rather than assembled by hand each cycle.
Curriculum and skills alignment
Programmes compared with employer and labour-market evidence.
Programme impact evidence
Learner and employer outcomes joined to programme data.
Where AI earns its place
AI opportunities
Service answers from approved sources
Students and staff get consistent answers from current policy, cited.
Professional services with less rekeying
AI used where it removes repetitive work, with people deciding.
Skills intelligence
Employer and labour-market evidence brought to curriculum decisions.
Safe AI for staff and students
A controlled alternative to public tools, on the institution’s own knowledge.
What usually sits underneath
When AI disappoints, the cause is usually the data, systems and ownership beneath it, so we look there too.
- Several student, finance and HR systems with different definitions
- Central and local copies of the same policy
- Data collected for returns rather than decisions
- Identity and access rules that differ between staff and students
Broader technology and data work
- Data strategy and data platforms
- Operating model and professional-services redesign
- Independent review of a student system or AI vendor
- Senior technology and data leadership
What we could build or change
Examples of the work
Illustrations of what an engagement could produce, scoped to your own information and measured against a baseline.
- A cited enquiry assistant over current policy and guidance
- A data model that joins programme, learner and outcome data
- A skills intelligence view that compares programmes with employer demand
- An AI adoption plan with policy, guidance and a safe internal tool
Answerable
Answerable STEM for skills intelligence
Answerable STEM is set up for STEM participation, outreach impact and skills evidence, with cohort data joined to the publications. It is shown here as an illustration on example content.
See every configurationAnswerable STEM: STEM & Skills Intelligence
Set up for STEM participation, outreach impact and skills evidence, with cohort data joined to the publications.
Collections
- Programme documents
- Labour-market data
- Employer feedback
Access
Only collections this user is permitted to see
Which engineering skills are employers asking for that our current programmes do not cover?
- Employer feedback repeatedly mentions data and automation skills 1
- Two programme specifications cover the topic only as an optional module 2
- Regional labour-market data shows demand rising for these roles 3
Grounded in 3 sources · every point cited
Sources
- 1Employer feedback summaryLatest cycle · theme 2
- 2Programme specificationEngineering · module list
- 3Regional skills dataOccupation table
Illustrative content. The configuration is real; the question, collections and sources are examples.
Proof
What we have built and done
Each item says where it comes from: TechGuidr’s own products, the founder’s earlier roles or demonstration configurations.
- Founder’s earlier role
Five thousand people remote overnight
When the pandemic hit, a leading UK university needed more than 5,000 staff and students working and learning remotely, at speed. Dan helped deliver a scalable desktop platform on Azure. Delivered in an earlier role.
See earlier work - Founder’s earlier role
A SaaS platform and its iOS and Android apps, rebuilt
Delivered by Dan in an earlier technology leadership role: the SaaS application and its mobile apps rebuilt around a Databricks lakehouse, with AI performance coaches embedded. Dashboard queries went from four to five seconds to under 600 milliseconds.
Read the case study - Demonstration configuration
Answerable STEM: STEM & Skills Intelligence
A configuration for curriculum, labour-market and programme evidence, shown on illustrative content.
See the configurations
How we engage
Where to start
Every engagement starts from the decision or outcome you need. Prices are starting points and exclude VAT.
- AI Work Reviewfrom £7,500Ten working days: where expert time and value leak, the top three opportunities, readiness, controls, economics and a prioritised roadmap.
- AI Work Sprintfrom £25,000One working workflow on your own information, with citations, permissions, expert review, evaluation and before-and-after measurement.
- Discoveryfrom £7,500Findings, options and a recommendation as a standalone deliverable. Typically one to three weeks.
- Leadership as a Servicefrom £7,500 a monthSenior technology, data and AI leadership on an agreed monthly remit. Full-time placement is separate.
- Independent Reviewfrom £7,500Independent challenge of a proposal, architecture, AI programme or business case. The answer can be "don't".
Industries
Related industries
- Research & insightResearch firms, analysts, market and economic intelligence, proprietary data businesses and specialist advisers.Explore research & insight
- Professional servicesLegal, accounting, consulting and specialist advisory firms whose product is expert time and judgement.Explore professional services
- Technology & softwareSoftware, SaaS and technology businesses building AI into products and platforms that need to scale.Explore technology & software
Start with the questions your service teams answer every day
We will look at where the answers live, what it would take to make them consistent and where AI helps safely.