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Solvexa Systems AI & Software

AI engineering

AI solutions built to be measured, not assumed

We work across generative AI, retrieval systems, and classical machine learning. Each service below states what it is, the problems it solves, what you receive, and where its limits are.

On this page

Ten areas of AI work

Jump to the service that matches your situation, or read through in order.

01

Generative AI applications

Language-model features built into a product, scoped to tasks where the output is checkable.

What it is

Software features powered by a large language model: drafting text, summarising long inputs, extracting structured fields, classifying records, rewriting content in a fixed format, or answering questions from a defined source. The model is one component inside an ordinary application, with validation, storage, and a user interface around it.

Business problems it can solve

  • Staff spend hours rewriting the same kinds of documents, replies, or summaries.
  • Information arrives as free text and has to be re-keyed into a system by hand.
  • A backlog of unstructured content is never reviewed because reading it is too slow.
  • A first draft is needed quickly, and a person can correct it faster than they can write it.

Suitable use cases

  • Summarising support threads, meeting notes, or long reports into a fixed template.
  • Turning free-text submissions into structured records that a system can process.
  • Drafting responses that a person reviews and sends.
  • Classifying and tagging incoming content so it reaches the right queue.

Typical deliverables

  • A working feature inside your application, with the model calls isolated behind a clear interface.
  • Prompt templates kept in version control, with the reasoning behind each one written down.
  • An evaluation set of real examples and the measured results against it.
  • Validation of model output before it is stored or shown, plus a defined fallback when a call fails.
  • Token and cost logging per feature, so spend is visible from the first day.

Important limitations

Language models produce plausible text, not verified facts. They are suited to tasks where a person or a rule can check the result, or where an occasional error is recoverable. They are not suited to calculations, financial totals, or decisions that must be exactly reproducible — those belong in ordinary code.

Security and data

We define what data may leave your environment before any model is chosen. That can mean redacting fields, restricting the request to the minimum context needed, using providers with a no-training commitment, or running a self-hosted model. Requests and responses are logged so you can audit what was sent.

02

RAG and enterprise knowledge systems

Answers grounded in your own documents, with citations back to the source passage.

What it is

Retrieval-augmented generation, usually shortened to RAG. Your documents are split, indexed, and searched at question time; only the passages that match are passed to the model, which answers using that text and cites where each part came from. The quality of a RAG system depends far more on the retrieval layer and the source content than on which model sits at the end.

Business problems it can solve

  • Policies, specifications, and procedures are spread across file shares, wikis, and email.
  • Experienced staff answer the same internal questions repeatedly.
  • Keyword search misses documents that use different wording for the same concept.
  • Nobody can tell whether an answer came from the current version of a document.

Suitable use cases

  • An internal assistant that answers policy and process questions with citations.
  • Support agents querying product documentation while handling a case.
  • Technical teams searching specifications, contracts, or standards.
  • Onboarding assistance for new staff working through existing documentation.

Typical deliverables

  • A document ingestion pipeline with chunking, embedding, and scheduled re-indexing.
  • A retrieval layer tuned against real questions, with hybrid keyword and vector search where it helps.
  • Answers that cite source documents and sections, so a reader can verify them.
  • Permission filtering, so retrieval respects who is allowed to see which document.
  • A retrieval test set with measured hit rate, plus a documented process for adding new sources.

Important limitations

A RAG system can only be as accurate as the documents behind it. Outdated, contradictory, or missing content produces confident but wrong answers, so document cleanup is often the first real task. Answers should be treated as a fast route to the source, not as a replacement for reading it in regulated or contractual contexts.

Security and data

Retrieval is filtered by user permissions rather than assuming everyone may see everything indexed. Documents stay in storage you control, indexes can be hosted in your own environment, and every query and returned passage can be logged for review.

03

AI assistants

Conversational interfaces over a defined scope of company data and actions.

What it is

A chat or search interface that sits on top of a specific, bounded domain: a product, a department, a dataset, or a workflow. An assistant is worth building when the underlying questions are varied enough that a form or dashboard cannot cover them, and when the answer already exists somewhere in your systems.

Business problems it can solve

  • Users cannot find a feature or a figure without asking a colleague.
  • Reporting requests queue behind an analyst for simple questions.
  • Customers ask the same product questions that are already answered in documentation.
  • Staff switch between several systems to answer one question.

Suitable use cases

  • An internal knowledge assistant for policies, processes, and product details.
  • A support assistant that drafts a reply and links the sources it used.
  • A data assistant that answers defined questions from reporting tables.
  • A guided assistant that collects information and creates a record in an existing system.

Typical deliverables

  • A chat interface with conversation history, source links, and a clear scope statement.
  • A defined set of tools or queries the assistant is allowed to use.
  • Handling for out-of-scope questions, including a route to a human.
  • Conversation logging with a feedback control so weak answers can be found and fixed.
  • Rate limits and cost controls per user and per period.

Important limitations

An assistant should have a narrow, stated scope. Broad "ask anything" assistants tend to answer confidently outside their knowledge, which erodes trust quickly. We prefer a smaller scope answered well, expanded once measurements support it.

Security and data

Each conversation runs under the signed-in user, so the assistant can only reach data that user could already open. Prompts and retrieved context are logged, and sensitive fields can be masked before a request leaves your environment.

04

AI agents and workflow automation

Multi-step automation that uses your systems, with permissions, logs, and approval points.

What it is

A process that reads an incoming request, decides which steps are needed, calls the relevant systems, and produces a result or a draft action. In practice, the most reliable version is mostly deterministic: fixed steps, defined tools, and a model used only where judgement or language handling is genuinely required.

Business problems it can solve

  • A routine process moves between three or four systems and a person copies data between them.
  • Requests wait in a queue because triage is manual.
  • Handovers between teams are tracked in email and get lost.
  • The same set of checks is performed by hand on every incoming record.

Suitable use cases

  • Triaging and routing incoming requests, with a drafted first response.
  • Collecting data from several systems into a single prepared summary or record.
  • Preparing an action — an order, a ticket, a report — for a person to approve.
  • Monitoring a queue and escalating cases that match defined conditions.

Typical deliverables

  • A workflow definition showing every step, tool, and decision point.
  • Scoped credentials per tool, so the agent can only perform the actions it needs.
  • A full audit trail of inputs, tool calls, outputs, and approvals.
  • Approval gates before any action that is costly, external, or hard to reverse.
  • Retry, timeout, and failure handling, with alerting when a run does not complete.

Important limitations

Fully autonomous agents are unpredictable in proportion to how much freedom they are given. We keep the number of open-ended decisions small, require approval before irreversible actions, and use plain code for any step that has a correct answer. Agents that write to production systems without review are not something we recommend.

Security and data

Every tool an agent can call is explicitly listed and given its own least-privilege credential. Actions are logged with the run that triggered them, and destructive operations require a human approval step.

05

Intelligent document processing

Turning PDFs, scans, and email attachments into structured, checkable data.

What it is

A pipeline that ingests documents, extracts the fields you care about, validates them against your rules, and routes anything uncertain to a person. It typically combines text extraction or OCR, layout handling, a model for the fields that vary, and deterministic validation for the fields that must be exact.

Business problems it can solve

  • Invoices, forms, or delivery notes are typed into a system by hand.
  • Documents arrive in inconsistent formats from many senders.
  • Errors from manual entry are found late, during reconciliation.
  • Processing time depends entirely on how many people are available that day.

Suitable use cases

  • Invoice and purchase order capture with supplier matching.
  • Application or claim forms turned into structured records.
  • Contract review that pulls out dates, parties, and defined clauses.
  • Identity and compliance documents checked against expected fields.

Typical deliverables

  • An ingestion pipeline for the document sources you use, including email and upload.
  • Field extraction with a confidence score attached to each field.
  • Validation rules — formats, totals, cross-checks against your master data.
  • A review queue where low-confidence documents are corrected by a person.
  • Accuracy measured per field on a labelled sample, reported before go-live.

Important limitations

Extraction accuracy varies by document quality and layout consistency. Poor scans, handwriting, and unusual formats need a review step. We design for a measured accuracy target with human review for the remainder, rather than assuming every document will be processed unattended.

Security and data

Documents often contain personal or commercial data. We agree retention periods, restrict access by role, and can keep processing inside your own infrastructure where the content cannot be sent to an external provider.

06

Predictive analytics

Forecasting and scoring from your historical data — starting with whether the data supports it.

What it is

Machine learning applied to your own records: demand forecasting, churn or risk scoring, anomaly detection, prioritisation. The first stage is always an assessment of whether the available history is complete and consistent enough to support a useful prediction. If it is not, we say so before any model work begins.

Business problems it can solve

  • Stock and capacity planning are based on last month repeated forward.
  • Problem accounts are only noticed after they have already left.
  • Anomalies in operational data are found by chance rather than by monitoring.
  • Prioritisation is done by whoever shouts loudest rather than by expected impact.

Suitable use cases

  • Demand and inventory forecasting.
  • Churn, risk, or lead scoring feeding an existing workflow.
  • Anomaly detection in transactions, sensor readings, or usage data.
  • Maintenance and capacity planning from historical operational data.

Typical deliverables

  • A data assessment covering coverage, quality, leakage risk, and realistic expectations.
  • A baseline model and comparison against the current manual approach.
  • Measured accuracy on held-out data, reported honestly including where it is weak.
  • Deployment as an API or scheduled job that writes results into your systems.
  • Monitoring for drift, with a defined retraining process and owner.

Important limitations

A prediction is a probability, not a fact, and accuracy depends on the past resembling the future. Models degrade when conditions change and need monitoring and retraining. Some questions cannot be answered from the data an organisation holds — that is a finding worth knowing early, not a failure.

Security and data

Training data is handled under agreed access rules and kept in your environment where required. Where predictions affect people, we document the inputs used and keep a record of the model version behind each stored score.

07

Recommendation systems

Ranking and suggestion features tied to a measurable outcome.

What it is

Systems that rank items for a user or a context: related products, relevant content, next best action, matching between two sides of a marketplace. Approaches range from straightforward rules and popularity ranking to collaborative filtering and embedding-based similarity, chosen by the data available rather than by novelty.

Business problems it can solve

  • Catalogue depth is wasted because users only ever see the first page.
  • Manual merchandising cannot keep up with the number of items.
  • Users abandon a search because nothing relevant appears near the top.
  • Cross-sell suggestions are static and ignore recent behaviour.

Suitable use cases

  • Related and complementary item suggestions in a catalogue.
  • Content ranking for a feed, library, or resource centre.
  • Next-best-action prompts for internal sales or service teams.
  • Matching supply and demand in a two-sided platform.

Typical deliverables

  • Event tracking for the interactions the model needs, defined before any model work.
  • A ranking service with a documented fallback for new users and new items.
  • Offline evaluation and an online test plan against the current behaviour.
  • Controls to pin, exclude, or boost items where the business needs to override.
  • Reporting that ties recommendation performance to the outcome you care about.

Important limitations

Recommenders need interaction data. With a small catalogue or low traffic, a well-designed rule set will often match a model at a fraction of the cost, and we will recommend that instead. Cold-start cases always need a defined fallback, and popularity feedback loops need active management.

Security and data

Behavioural data is personal data in most jurisdictions. We keep identifiers separated where possible, apply your retention policy, and make the tracked events explicit so your privacy notice can describe them accurately.

09

AI integration with existing software

Adding AI capability to systems you already run, without a rewrite.

What it is

Work that adds AI features to an application already in production. That usually means an integration layer beside the existing system rather than changes throughout it: a service that receives a request, handles the model call, validates the result, and returns it in the shape the existing code expects.

Business problems it can solve

  • A useful AI feature is blocked because nobody wants to touch the legacy codebase.
  • A model was added directly in application code and is now impossible to test or cost-control.
  • Several teams call model providers separately, with no shared logging or limits.
  • A proof of concept works on a laptop but has no path to production.

Suitable use cases

  • Adding summarisation, classification, or extraction to an existing product.
  • Putting a shared internal service in front of model providers, with logging and quotas.
  • Taking a working prototype through to a deployed, monitored service.
  • Replacing a brittle rule set with a model where the rules can no longer keep up.

Typical deliverables

  • An integration service with a stable interface, so provider changes do not reach your application.
  • Caching, rate limiting, retries, and timeouts around every external model call.
  • Central logging of prompts, responses, latency, and cost per feature.
  • A rollout plan with feature flags and a documented way to switch the feature off.
  • Documentation your own developers can work from afterwards.

Important limitations

Some legacy systems have no safe extension point, and the honest answer is to modernise a part of the system first. We will say when that is the case rather than bolting a feature onto something that cannot support it.

Security and data

The integration layer is where redaction, allow-lists, and audit logging belong. Provider keys stay server-side and are never exposed in browser code.

10

AI evaluation and optimization

Measuring quality, cost, and latency of an AI feature that is already live.

What it is

A structured review of an existing AI feature: how often it is right, what it costs per request, how long it takes, and where it fails. The output is a measured baseline and a prioritised list of changes — prompt structure, retrieval quality, model choice, caching, or replacing part of the flow with deterministic code.

Business problems it can solve

  • The feature works in demonstrations but users report inconsistent results.
  • Model spend rises each month and no one can attribute it to a feature.
  • Nobody can tell whether a prompt change made things better or worse.
  • Response times are slow enough that people avoid the feature.

Suitable use cases

  • Establishing an evaluation set before a planned model or provider change.
  • Reducing cost per request while keeping measured quality steady.
  • Diagnosing why retrieval quality dropped after a content migration.
  • Preparing an AI feature for a security or compliance review.

Typical deliverables

  • An evaluation set built from real usage, with agreed scoring criteria.
  • A measured baseline for accuracy, cost, and latency.
  • A regression check that can run before each release.
  • A prioritised improvement list with the expected effect of each change.
  • Dashboards for spend, error rate, and latency, with alert thresholds.

Important limitations

Evaluation reduces uncertainty; it does not eliminate it. Model providers change behaviour between versions, which is exactly why a regression check matters. We report measured numbers with their sample size rather than a single accuracy figure with no context.

Security and data

Evaluation sets often contain real customer data. We agree on anonymisation and storage location before anything is collected, and keep evaluation data under the same access controls as production.

Honest scoping

When AI is not the right solution

A model is one option among several, and often not the best one. These approaches are cheaper to run, easier to audit, and produce the same answer every time — we will recommend them where they fit.

  • Standard automation

    If a process follows the same steps every time, a scheduled job or an integration is cheaper, faster, and produces the same result on every run.

  • Business rules

    Eligibility, pricing, approval thresholds, and compliance checks must be exact and auditable. Explicit rules can be reviewed by the people who own them; a model cannot.

  • Search and filtering

    Many "AI assistant" requests are really a search problem. A good index with filters answers the question faster, at lower cost, and with results the user can verify.

  • Database queries and reporting

    Counts, totals, and trends should come from a query, not a language model. Deterministic reporting is exact, reproducible, and far cheaper to run.

  • Better data entry and validation

    If data arrives incomplete, fixing the form and its validation removes the problem at source instead of paying a model to guess what was meant.

  • Conventional software development

    Most business problems are workflow, integration, and interface problems. Building the system properly usually delivers more value than adding a model to a broken process.

Our first question is never “which model?” — it is what result you need, how it will be checked, and what happens when the system is wrong. If the honest answer is that AI adds cost without adding value, we will say so, and propose the alternative that does the job.

Not sure whether AI fits your problem?

Describe the process you are trying to improve. We will tell you whether an AI approach is worth testing, and what would need to be true for it to work.

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