Techbellys / The Core Where Technology is Built
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// The pilot has been running long enough

We don’t ship prototypes. We ship systems that stay in production.

Most AI work dies somewhere between a promising notebook and a system people can rely on. We take the initiative that's been parked in a slide deck and put it live — agents that take real actions, retrieval that cites its sources, pipelines that hold under load, and the monitoring that keeps all of it honest after launch.

No pilots that never graduate. No "phase two, TBD."
50+
Projects delivered
98%
Client satisfaction
10+
AI models in production
24/7
Support & monitoring

Featured builds

// Start here

Six systems we build, and the mechanism inside each one that makes it trustworthy. The full capability menu is further down.

01
Data Engineering & Pipelines

Messy source data in. A table your analysts can trust out — every hour, unattended.

Records arrive from whatever the business already runs on — exports, APIs, third-party feeds — and land in a warehouse in a shape people can actually query. Bad rows are quarantined with the reason attached rather than silently poisoning the numbers downstream.

How it works: Every stage is tested and idempotent, so a failed run is simply re-run instead of untangled by hand at midnight.What it shows: The unglamorous layer that everything else on this page depends on.
SOURCESINGESTVALIDATETRANSFORMWAREHOUSEQUARANTINE
02
Generative AI & LLM Integration

Ask your documents a question. Get an answer with the source attached.

Contracts, policies, manuals and reports become something your staff can question in plain words. Every answer carries the passage it came from, so the reader can verify it rather than take it on trust.

How it works: Retrieval is scored before anything is generated. When nothing clears the threshold, the system says it does not know instead of writing something plausible.What it shows: Grounded answers — and a refusal path, which is what makes the rest believable.
DOCUMENTSCHUNK + EMBEDRETRIEVE + SCOREGENERATEANSWER + CITATIONREFUSE
03
AI Agents & Chatbots

An agent that doesn’t just reply. It goes and does the work.

It watches a queue, an inbox or a webhook, works out what the request actually needs, and then acts on your real systems — creating the record, updating the ticket, sending the response.

How it works: Anything irreversible stops at an approval gate, and every tool call is written to an audit log you can replay after the fact.What it shows: Agents that take actions rather than make conversation — with a brake fitted.
TRIGGERPLANTOOL CALLSWRITE BACKAUDIT LOGAPPROVAL
04
Machine Learning Solutions

A forecast your planners are actually willing to run on.

Demand, churn, risk or capacity predicted from your own history and delivered into the tool the team already opens each morning — not a notebook that nobody launches.

How it works: Backtested against the rule of thumb it replaces, so the improvement is measured against how you work today rather than against nothing.What it shows: Models judged by the decision they change, not by a metric in isolation.
HISTORYFEATURESTRAINBACKTEST vs BASELINESERVE
05
MLOps & AI Infrastructure

Improving a model stops being a project every single time.

A change to a model or a prompt travels the same road as any other code: evaluated automatically, promoted only through a gate, and released to a slice of traffic before it reaches all of it.

How it works: Quality and drift are watched after release, and a version that degrades is rolled back without waiting for someone to notice.What it shows: The part that keeps working long after the launch announcement.
COMMITEVAL GATEREGISTRYCANARYPRODUCTIONROLLBACK
06
AI Consulting & Strategy

A roadmap that ends in a deployment, not a document.

We audit what your data can genuinely support, score the candidate use cases by impact against effort, and then build the strongest one thin and end-to-end — instead of writing a phased plan for somebody else to attempt later.

How it works: That first slice goes live early, which is what turns an assumption into evidence before the budget is committed to it.What it shows: Strategy measured by what shipped from it.
DATA AUDITSCORE USE CASESTHIN SLICE LIVESCALE WHAT WORKS

The capability menu

// 24 capabilities · 6 service lines

Everything we deliver, grouped the way we actually scope work. See something that matches the problem you're sitting on? Start there.

AI Consulting & Strategy
01

AI readiness assessment & data audit

An honest read on whether your data can carry the thing you want to build — before anyone writes code.

02

Use case identification & prioritisation

Every candidate idea ranked by impact against effort, so the first build is the one that pays.

03

Technology stack recommendation

The architecture decision made once, in the open, with the trade-offs written down.

04

ROI modelling & business case

The number your CFO needs, built from your costs rather than a vendor's benchmark.

Machine Learning Solutions
05

Predictive analytics & forecasting

Demand, churn, risk and capacity models trained on your history, scored against the baseline they replace.

06

Natural language processing

Classification, extraction and summarisation over the text your business already generates.

07

Computer vision & image recognition

Inspection, detection and document understanding where a person is currently doing the looking.

08

Recommendation systems

Ranking and personalisation that lifts a metric you already track, measured against the status quo.

Generative AI & LLM Integration
09

LLM fine-tuning & prompt engineering

Getting a foundation model to behave on your domain — and knowing when fine-tuning isn't the answer.

10

RAG pipelines

Retrieval-augmented generation that grounds every answer in a real source and says so when it can't.

11

AI-powered content generation

Drafting at volume inside your tone, format and approval process — not around it.

12

Enterprise knowledge assistants

One place to ask what the company already knows, with permissions the assistant actually respects.

AI Agents & Chatbots
13

Autonomous agents for workflow automation

Agents that read your systems, decide, and write back — with the approval gate where you want it.

14

Support chatbots with memory

Conversations that carry context across turns and sessions, and escalate cleanly when they should.

15

Multi-agent orchestration

Several specialised agents coordinating on one job, with a supervisor that can stop the run.

16

Voice AI & conversational interfaces

Spoken interfaces for the places a keyboard was never going to work.

Data Engineering & Pipelines
17

ETL / ELT pipeline design

The unglamorous layer everything else depends on, built to be debugged at 3am by someone else.

18

Real-time & batch processing

Streaming where latency matters, batch where it doesn't, and a clear reason for each choice.

19

Warehouse & lake architecture

Storage and modelling designed for the questions you'll ask next year, not just this quarter.

20

Data quality monitoring & governance

Tests and lineage that catch a bad upstream change before it reaches a model or a dashboard.

MLOps & AI Infrastructure
21

Model deployment & serving

From notebook to endpoint, versioned and rollback-able, with load it can actually take.

22

CI/CD for ML pipelines

Retraining, evaluation and release automated — so improving a model isn't a project each time.

23

Monitoring & drift detection

Alerts when the world moves and the model quietly stops being right.

24

Cost optimisation & auto-scaling

Inference bills that track usage instead of surprising you, and capacity that follows demand.

How the work runs

// Four stages · one team

The same methodology on every engagement, so you always know which stage you're in and what comes out of it.

01

Discover

Deep-dive workshops on your business, your data and what success actually has to look like.

02

Design

The architecture, the milestones and the success criteria — agreed before the build starts.

03

Develop

Build, train and iterate against continuous feedback and validation, not a big reveal at the end.

04

Deploy

Into production with monitoring, support and a plan for the improvements that come after.

// The stack behind the outcomes — so your team doesn't have to hold it
PythonPyTorchscikit-learn FastAPILLMs / GPT / ClaudeRAG & embeddings Vector DBsn8nAirflow PostgreSQLSparkdbt DockerKubernetesAWS / GCP MLflow
// Bring the initiative that's been parked since last quarter

Let's put it in production. Not on the roadmap.

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