Technology Solutions
AI-Powered Solutions
Practical uses of language models: search your documents, classify, draft, with people in the loop.
03 / Technology Solutions6 capabilities · 7 deliverables
The problem
AI is easy to demo and hard to depend on
A language model can produce a convincing answer in seconds, which makes a prototype look finished. The difficulty shows up afterwards. Models sometimes state things that are false with complete confidence, they behave differently on inputs you did not test, and the cost per request can climb quickly once real volume arrives. A chatbot pointed at your website with no controls may quote a policy that does not exist.
Many businesses also reach for AI where a simpler tool would do. If the task is a fixed rule, a database query or a template, code is cheaper, faster and fully predictable. The useful cases are narrower: finding information in large piles of unstructured text, sorting messages into categories, summarising, and producing first drafts that a person then checks. Success depends on choosing those cases carefully and building the safeguards around them.
Sound familiar?
- Staff spend hours searching old contracts, manuals or emails for a single answer
- Incoming enquiries or tickets are sorted by hand before anyone can act on them
- An experiment with a chatbot gave a wrong answer and nobody trusts it now
- You are not sure whether an AI feature is worth its running cost
Our solution
Narrow, checkable AI features with the safeguards built in
We begin with a specific task and a way to judge it. For questions over your own material, we build retrieval: your documents are split, indexed and searched, and the model answers only from the passages found, with citations back to the source so a person can verify. For classification, we compare a model against your existing labels. For drafting, such as replies or summaries, the output goes to a person for approval before it is sent or saved.
We then evaluate on real examples from your business. We assemble a test set, measure how often the system is right on that set, review the failures with you, and repeat after each change. We do not quote accuracy figures in advance, because they depend on your data. We also plan for failure: low-confidence answers are routed to a human, prompts and inputs are logged, personal data is handled according to your policy, and token costs are estimated and capped. Where AI is not the right tool, we say so and use something simpler.
Capabilities
What's inside.
- 01
Retrieval over your documents
Search and question-answering across policies, contracts, manuals and past tickets, with answers grounded in retrieved passages and linked to the original source.
- 02
Classification and extraction
Sorting emails, tickets or forms into categories and pulling out fields such as dates, amounts and names, checked against labels your team has already assigned.
- 03
Drafting with human review
First drafts of replies, summaries and reports prepared for a person to edit and approve, with the approval step kept in the workflow rather than skipped.
- 04
Evaluation and test sets
A set of real examples with expected outcomes, run after every change, so you know whether a prompt or model update helped or hurt.
- 05
Guardrails and failure handling
Confidence thresholds, fallbacks to a person, refusal rules for out-of-scope questions, input and output logging, and handling of sensitive or personal data.
- 06
Cost and latency control
Model selection by task, caching, prompt length limits and usage caps, with a cost-per-request estimate before you commit to volume.
Process
How the work unfolds.
- Step 01
Use case selection
We pick one task with clear inputs, a definable right answer and a person who can check the output. We also rule out ideas where a rule or query would do the job better.
- Step 02
Data review and test set
We examine the documents or messages involved, clean what is needed, and build a test set of real cases with your team's expected answers.
- Step 03
Prototype and evaluation
A working version is run against the test set. We review the errors together, adjust the design, and decide whether the results justify going further.
- Step 04
Production, review loop and monitoring
Integration into your tools with human review where needed, usage and cost tracking, and a regular look at flagged failures to improve it over time.
You receive
Deliverables
- 01Written assessment of suitable and unsuitable use cases
- 02Working AI feature integrated into your tools or site
- 03Document index and retrieval pipeline with source citations
- 04Evaluation set and a results report on your own data
- 05Human review workflow for drafted or low-confidence outputs
- 06Cost estimate, usage caps and monitoring dashboard
- 07Documentation of prompts, models, data handling and known limits
Where it fits best
- E-commerceCatalogue structure, product-page SEO, checkout fixes, paid media and the integrations that keep stock, orders and finance in agreement.
- HealthcareLocation pages, booking flows, reviews and patient communication, built with privacy and the plain language patients actually search in.
- EducationProgramme discovery, enquiry-to-enrolment funnels, and the CRM plumbing that stops prospective students slipping between teams.
- Professional ServicesAuthority-led content, specialism pages that rank, intake forms that qualify and CRM workflows that make referrals measurable.
- Startups & SaaSProduct sites, demo funnels, onboarding flows and growth instrumentation, shipped at the pace a young product needs.
FAQ
Questions we get asked.
Will the AI make things up?
Language models can produce statements that are wrong, and no setup removes that risk completely. We reduce it by answering only from retrieved source text, showing citations, declining questions outside scope, and keeping a person in the loop for anything consequential. We also test on your real cases to see how it fails.
Is our data safe? Will it be used to train a public model?
That depends on the provider and plan, so we check the data terms before choosing one. Business and API tiers from major providers generally do not train on your inputs by default, but we confirm it in writing for your case. For sensitive material we can minimise or mask personal data, restrict access, or use a model hosted in a controlled environment.
How much will it cost to run?
Cost depends on the model, the length of the text processed and the volume. We estimate it from your actual documents and expected usage before you commit, and add caps and monitoring so a spike does not become a surprise bill. Simple tasks can often use a smaller, cheaper model.
Can AI replace a team member?
We do not build it with that goal. The reliable uses speed up people by finding information, sorting inputs or preparing drafts, while a person stays accountable for the decision. Tasks that are high stakes, ambiguous or relationship-driven should remain with people.
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Ready to talk about ai-powered solutions?
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