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AI Data & Automation

Put the data you already own to work

Most organisations are sitting on years of documents, tickets, transactions and logs that nobody can query usefully. We turn that material into retrieval systems, forecasts and automations that remove manual work from the day.

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A senior engineer replies within 24 hours — not a sales rep.

Why the data you have isn't paying you back

The answer exists somewhere and nobody can find it

It's in a PDF on SharePoint, or in a Confluence page last edited in 2021, or in a thread someone archived. Your search box matches on keywords, so it returns forty documents and none of the right one. Staff give up and ask a colleague, which is why the same question gets answered by hand over and over.

Three systems disagree about the same customer

The CRM has one address, billing has another, support has a third with a typo. Any analysis built on top inherits the contradiction, and the first executive who spots two different totals in two different dashboards stops trusting both. No model fixes this — it has to be resolved in the data.

Forecasts get produced and then ignored

A monthly spreadsheet forecast lands, planners glance at it, and then order the way they always have. Usually because the forecast has no error history anyone can inspect, arrives too late in the cycle to change a purchase order, and offers no explanation for the numbers it produces.

The manual work is invisible in the accounts

Nobody has a line item for the four hours a week spent copying invoice fields into the ERP, or the daily export-reconcile-reupload ritual between two systems that were never integrated. It doesn't look expensive until you total it across a department and notice it's a full role.

How we approach data and automation work

The unglamorous half of this is data quality, and skipping it is the single most reliable way to build something people abandon in month two. We do that part first and we're upfront that it is not the fun part.

Profile the sources before promising anything

We look at what is actually in your systems: duplicate rates, null density, how far back the history really goes, whether the timestamps mean what the schema says. This regularly changes the scope, and it is far cheaper to change scope now than to discover the gap after a model has been trained on it.

Retrieval quality is measured, not assumed

For any system that answers from your documents, we build a question set with known correct sources and score whether the right passage is retrieved before we care what the generated answer sounds like. A fluent answer over the wrong document is the most dangerous output a retrieval system produces.

Automate the path, keep a human at the risky junction

Full automation is right for high-volume, low-consequence steps. Where a mistake costs money or reputation, we automate the preparation and route the decision to a person with everything they need on one screen. That keeps throughput while leaving accountability with someone who can be asked about it.

Every automated run leaves a trail

What triggered it, what it read, what it decided, what it wrote back and who could have overridden it. When a downstream figure looks wrong six weeks later, the reconstruction takes minutes. Systems without this get switched off the first time they are questioned.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

RAG Pipeline Development

Retrieval-augmented generation lets a language model answer from your own documents instead of its training data, with citations you can check. It suits policy manuals, product documentation, contracts and support histories — anywhere the correct answer already exists in writing but is hard to locate.

Document parsing that survives tables, scanned PDFs and multi-column layouts
Chunking and embedding strategy tested against a scored retrieval set, not chosen by default
Hybrid keyword and vector search with reranking, plus permission filtering so users only retrieve what they may see
Citations to the source passage, and a defined refusal path when nothing relevant is found

Predictive Analytics

Forecasting and scoring over your historical records — demand, churn, delivery time, failure risk, credit exposure. The value comes from a prediction arriving early enough and explained well enough that someone actually changes a decision because of it.

Backtesting against held-out history so accuracy is known before anyone relies on it
Feature engineering from transactional, seasonal and external signals
Prediction intervals rather than single numbers, so planners can see the uncertainty
Delivery into the tool where the decision is made — ERP, planning sheet or dashboard

AI Automation Systems

End-to-end automation of the repetitive office work that sits between your systems: reading an incoming document, extracting the fields, validating them, writing them into the system of record, and escalating the ones that fail a check. Distinct from agents in that the sequence is fixed and defined by you.

Document intake from email, portals or shared drives with classification and routing
Field extraction with confidence scoring and an exception queue for anything doubtful
Write-back into ERP, CRM or accounting systems via API, with idempotency so retries do not duplicate records
Run history, exception rates and time-saved reporting per workflow

Chatbot Development

Text assistants in the channels your users already occupy — website, in-app, WhatsApp, Slack, support desk. A chatbot answers and looks things up within a scope you define; it is not an autonomous agent making decisions, and being clear about that boundary is what keeps it trustworthy.

Grounded answers drawn from your own content, with a plain admission when the answer is not there
Handover to a human agent carrying the full conversation, not a fresh ticket
Read-only account lookups through authenticated APIs — order status, balance, booking details
Conversation analytics showing the questions it fails on, so content gaps get closed

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Retrieval & vector search

Postgres with pgvector
Pinecone
Weaviate
Qdrant
Elasticsearch
LlamaIndex
LangChain

Data pipelines

Apache Airflow
dbt
Apache Kafka
Pandas
Spark
Snowflake
BigQuery

Modelling & analytics

scikit-learn
XGBoost
Prophet
statsmodels
Metabase
Power BI

Automation & integration

Python
FastAPI
Celery
Temporal
n8n
REST APIs
Webhooks

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Source audit and quality baseline
We inventory the systems in scope and measure what condition their data is in — duplicates, gaps, contradictions between systems, and how much history is genuinely usable. You get an honest read on which of your intended use cases the data currently supports.
You get: A source inventory with a quality score per system, the specific defects blocking each use case, and a prioritised remediation list.
02
Pipeline and ground truth
We build the ingestion and cleaning path, then assemble a labelled set your own team validates — correct answers for retrieval, historical outcomes for forecasting, worked examples for automation. Nothing gets optimised until there is something to optimise against.
You get: A running ingestion pipeline with monitoring, and a validated ground-truth dataset signed off by your subject-matter experts.
03
Build against the score
Chunking strategy, embedding choice, reranking, feature selection, rule thresholds — each is an experiment with a measured result. We keep what moves the number and discard what merely felt like an improvement in a demo.
You get: The working system meeting the accuracy threshold agreed in step one, plus an experiment log showing what was tried and how it scored.
04
Roll out beside the manual process
New automations run in parallel with the existing manual process for a defined period so discrepancies surface while the old path is still there to catch them. Only once the two agree does the manual step get retired.
You get: Production deployment, a parallel-run comparison report, operator documentation, and dashboards for throughput, exceptions and drift.

Where we've applied this

Logistics

Demand and ETA forecasting that accounts for seasonality and disruption, alongside automated extraction of line items from delivery notes and customs paperwork.

Finance

Policy and regulation search that cites the clause it answered from, plus reconciliation automation that flags only the exceptions worth an analyst's attention.

Healthcare

Retrieval across protocols and formulary documents where every answer links to the source page, and intake paperwork routed automatically to the right department.

Retail

Replenishment forecasting per SKU and location, and supplier catalogue ingestion that normalises inconsistent product data before it reaches your storefront.

Professional services

Searchable institutional memory across past proposals, contracts and project files, so a new hire can find the precedent without interrupting a partner.

Real estate

Lease and title document extraction into structured fields, with automated diary entries for break clauses and rent reviews that currently live in someone's calendar.

Clients we've built for

Real products, in production, with real users on them.

National Industries Company

Mobile Travelers

Lush

Sagor

Questions buyers ask us

Our documents are a mess. Is this still worth doing?

Usually yes, but the cleanup is part of the project rather than a prerequisite you have to complete alone. Scanned files, inconsistent templates and duplicate versions of the same policy are the normal starting condition. What genuinely blocks progress is contradiction — two current documents stating different things — because a retrieval system will cite one of them and sound certain. We surface those during the audit so an owner can decide which is authoritative.

What drives the cost here?

Data condition first, integration count second, accuracy bar third. Pulling from one clean database is inexpensive; reconciling four systems that identify customers differently is where the hours go. Ongoing cost differs by service too — retrieval systems carry a per-query inference charge, while a forecasting model is mostly a fixed retraining schedule. We model both during design.

How is a chatbot different from an AI agent?

Scope of action. A chatbot answers questions and performs read-only lookups inside boundaries you set; the worst case is a wrong answer. An agent decides a sequence of steps and takes actions that change state in your systems, so the worst case is a wrong action. They need different amounts of oversight, and conflating them is how teams end up with more autonomy than they intended. Our agentic work sits under Intelligent Agents.

Can you tell us upfront how accurate the forecast will be?

Nobody honest answers that before seeing the data. What we can commit to is telling you the accuracy early: backtesting against your own history gives a measured error range within the first phase, and if that range is too wide to be useful for your decision we will say so rather than build it. A forecast with a known error band beats a confident one with no track record.

Will this integrate with the systems we already run?

That is most of the work. We connect to ERPs, CRMs, ticketing tools, data warehouses and document stores through their APIs, and fall back to scheduled file exchange where a vendor offers nothing better. Where a legacy system has no usable interface at all we will say so early, because that constraint shapes the design more than any model choice does.

Who runs it after launch and who owns it?

You own the pipelines, models, prompts, evaluation sets and infrastructure code outright. Running it needs someone watching pipeline failures, exception queues and drift — many clients keep us on for that, others take it in-house with the runbook and monitoring we hand over. Either way the documentation assumes an engineer who was not on the project, because eventually that is who will be reading it.

Tell us where the manual work is

Describe the repeated lookup, the spreadsheet nobody wants to maintain, or the forecast you don't trust. A senior engineer replies within 24 hours with a view on what your data can realistically support — including when the honest answer is to fix the data first.

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