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Hexploits

Applied AI

AI decision support and forecasting

Decision support systems score, rank or forecast against a business’s own history so that people can act on the result: which invoices will be paid late, which candidates fit a role, which stock to order, which customers are about to leave. Hexploits builds these with explanations a manager can read and a measurement period agreed before the model is trusted.

Request a proposalTalk to an engineer

A named engineer replies within one working day. A written scope and an indicative price within two.

Who this is for

  • Finance directors who want forecasts with stated confidence rather than a spreadsheet extrapolation.
  • Operations leaders allocating people, stock or capacity.
  • Businesses with years of transaction history and no way to use it.

What you get

Deliverables, not slogans. Each one appears in the statement of work.

  • A data audit stating what history exists, its quality, and what it can and cannot support.
  • Models with per-prediction explanations in business terms.
  • A back-test against history showing what the model would have predicted and how it performed.
  • Integration into the tool where the decision is made, not a separate dashboard nobody opens.
  • Monitoring for drift, with retraining under managed support.

Our engineers work across the major languages, frameworks and cloud platforms. We build on the stack you already run, with technology choices explained in writing before work begins.

How it is delivered

The same four stages as every Hexploits engagement, applied to this capability.

  1. Stage 1

    Data audit

    Before any model, we establish whether the data can answer the question. Sometimes the honest answer is that it cannot yet.

  2. Stage 2

    Back-test

    The model is trained on history and tested on periods it has not seen. Results are reported against the simple baseline you use today.

  3. Stage 3

    Pilot

    Predictions run alongside real decisions for an agreed period; performance is reviewed with the people who make them.

  4. Stage 4

    Operate

    Drift monitoring, scheduled retraining and a monthly performance report.

How success is measured

Every engagement agrees its measures and the measurement period in writing before work starts.

  • Forecast error against the baseline method, over the agreed period.
  • Decision outcomes: late payments avoided, stock-outs reduced, placements made, measured after the pilot.
  • Time saved in preparing the decision.

Proof

Case studies with numbers, and reviews linked to Google where they were left there.

<100 msAPI response time at p95

JobVantage · Recruitment technology · Duration TBC

99.9% availability and sub-100ms responses for a recruitment intelligence platform, at negligible infrastructure cost

99.9%+ availability · <5 s ai processing time for complex media
75%Under the original budget

swarmd.ai · Software vendor · 6 months

Enterprise AI control plane delivered in six months at 75% under budget for a UK software vendor

75% ahead of the original timeline · 6 months from blank page to paying enterprise customers

  • Director, JobVantage

    “Working with Hexploits has genuinely been a pleasure, and I see them as my scaling partner for the foreseeable future as JobVantage grows. If you’re looking for a development team who combine strong AI/engineering capability with honesty, flexibility and a real interest in your business, I’d strongly recommend them.”

    Brandon BowdenDirector, JobVantageRead the review

  • Director, PeppaSync

    “Hexploits have been a breath of fresh air on Peppasync, an AI/ML autonomous decision platform for commercial leaders in retail and ecommerce. The depth and thoroughness the team brought to design and architecture was second to none.”

    Banky AlaoDirector, PeppaSyncRead the review

Questions we get asked

Is this machine learning or an LLM?
Usually conventional machine learning on structured data, which is cheaper, more accurate and easier to explain for scoring and forecasting. Language models are used where the input is text.
How do we know it is better than what we do now?
The back-test and the pilot both compare the model against your current method over the same period, and we report both numbers.
Does this fall under the EU AI Act?
It depends on the decision. Credit scoring, insurance pricing, hiring and some education uses are named high-risk. We classify the system during discovery and build the required documentation and oversight into the delivery.

Related

Sectors where this is most often needed

Reading

More in applied ai

Applied AI overview

Next step

Request a proposal.

Tell us about the system and the sector. A named engineer replies within one working day. A written scope and an indicative price within two working days of a short scoping call.

Request a proposalTalk to an engineer