AI & Machine Learning for
Mid-Market Enterprises

Q: Our data is a bit of a mess. Is it too early to talk?

No — that's where most teams start, and sorting it out is the first thing we do. We'd rather tell you early that an idea won't work with the data you have than spend months building around it. Most teams have more usable data than they think, just in worse shape than they'd like.

Q: Will it still work well a year from now?

We watch it. If the results start slipping, we're alerted before you notice it, and the system gets refreshed and re-checked before any update goes live. You get the dashboards and the step-by-step guides, so your team can keep an eye on it too.

Q: Do you use tools like ChatGPT, or build something custom?

Whichever actually suits the job. For things like forecasting or scoring, a simpler approach is usually faster and cheaper than a large AI model, and we'll tell you when that's the case. For anything involving language or documents, we start with proven tools and only build something bespoke when it clearly earns its place.

Q: Who owns what you build?

You do — all of it. The code, the models, the documentation, kept in your accounts from day one. Nothing important sits on our systems, and once we hand over, nothing depends on us to keep running.

Q: Can our own team look after this once you're gone?

That's what we aim for. Your team works alongside ours as we build rather than being handed a finished box, and we walk them through how it all works — and how it can go wrong — before we leave. If you'd like us to stay on to help, that's an option, not a requirement.

Most AI projects stall between the pilot and production. We build the part that lasts — data pipelines, model training, evaluation and monitoring — so predictions hold up against real traffic. Our teams ship production ML on PyTorch and scikit-learn, wrap it in FastAPI services your engineers can own, and instrument every model so accuracy drift is caught before your customers notice it.

The problem

Most AI pilots never reach production

The modelling is rarely what stops them. Projects stall because the data was never pipelined, nobody owned the model after the demo, and no one agreed up front what “working” would mean. We build for the part after the pilot.

Turning pilotsinto real impact

Engineeringthat lasts

Outcomes overactivity

Built for production.Backed by engineering.

~80%

of pilots never ship

Industry surveys put the share of AI proofs-of-concept that never reach production at roughly four in five. Almost all of them demo well.

The gap

is engineering, not research

A notebook that scores well on a static extract is perhaps a fifth of the work. Pipelines, serving, evaluation and monitoring are the rest.

Our fix

production shape from day one

We agree the success metric before writing code, build the pipeline alongside the model, and hand over something your engineers already know how to run.

HOW WE DO

Model Development & Research

End-to-end model research, architecture design and custom training for supervised, unsupervised and self-supervised tasks.

Data Engineering & Feature Ops

Scalable data pipelines, feature stores and preprocessing to ensure high-quality inputs for reliable model training.

MLOps & Deployment

Production-ready CI/CD for models, containerized serving, autoscaling and model versioning for safe, repeatable releases.

Computer Vision

Custom vision systems including object detection, segmentation and image understanding for automation and insight.

Natural Language Processing

Text understanding, search, summarization, and conversational agents using state-of-the-art NLP and LLMs.

Generative AI

Creative and productive generative solutions: multimodal generation, code assistants, prompt engineering and fine-tuning.

Predictive Analytics & Forecasting

Time-series forecasting, customer lifetime value, churn prediction and business KPIs driven by ML models.

Privacy, Security & On-Device AI

Differential privacy, secure model serving and optimized on-device inference for low-latency, privacy-preserving apps.

Explainability & Monitoring

Model interpretability, drift detection and observability to keep models transparent, fair and performant.

What changes

Outcomes we hold ourselves to

Every engagement starts by agreeing which of these numbers we're moving, and how we'll measure it. Ranges below reflect what our AI and ML engagements have delivered — your baseline determines where you land.

40–70%

less manual handling

On document- and ticket-heavy workflows, once extraction and classification are in production.

2–5×

faster time to a decision

Batch reports replaced by scoring that runs inline, so the answer arrives while it's still actionable.

< 200ms

typical inference latency

For real-time serving paths, measured at p95 under production traffic — not on a laptop benchmark.

Weeks, not quarters

to a working pilot

A scoped model against your real data inside the first engagement, so the business case is evidence rather than a slide.

How we work

Three ways to start

Most AI programmes don't fail on the modelling — they fail because the shape of the engagement never matched how much was actually known up front. Pick the one that fits what you know today; moving between them mid-programme is normal.

Scoped build

For a defined problem with data already in reach and a clear success metric.

Agreed metric and acceptance threshold before we start
Pilot to production in one continuous engagement
Full handover, including MLOps and monitoring

Timeline

8–16 weeks, fixed scope

Best for

A known, bounded problem

Trust & compliance

Built to survive an audit

AI systems that touch regulated data need more than accuracy. Governance is designed in from the first sprint rather than retrofitted when a review is scheduled.

Aligned to

GDPR

HIPAA

SOC 2

ISO 27001

EU AI Act

Data residency and isolation

Training and inference run inside your cloud tenancy and chosen region. Your data is never used to train anything outside your organisation.

Regulatory alignment

Engagements are run to fit GDPR, HIPAA and SOC 2 control requirements, with the documentation and evidence your auditors will ask for.

Explainability on record

Feature attribution on individual predictions, so a decision can be explained to a customer, a regulator or an internal review board.

Bias testing and review gates

Performance measured across subgroups, not just in aggregate, with human review required on decisions that carry material consequence.

Common questions

The things people usually ask us

Still have a question?Tell us what you’re weighing up and we’ll come back with a straight answer — usually inside a business day.

Talk to the team

No — that's where most teams start, and sorting it out is the first thing we do. We'd rather tell you early that an idea won't work with the data you have than spend months building around it. Most teams have more usable data than they think, just in worse shape than they'd like.

We watch it. If the results start slipping, we're alerted before you notice it, and the system gets refreshed and re-checked before any update goes live. You get the dashboards and the step-by-step guides, so your team can keep an eye on it too.

Whichever actually suits the job. For things like forecasting or scoring, a simpler approach is usually faster and cheaper than a large AI model, and we'll tell you when that's the case. For anything involving language or documents, we start with proven tools and only build something bespoke when it clearly earns its place.

You do — all of it. The code, the models, the documentation, kept in your accounts from day one. Nothing important sits on our systems, and once we hand over, nothing depends on us to keep running.

That's what we aim for. Your team works alongside ours as we build rather than being handed a finished box, and we walk them through how it all works — and how it can go wrong — before we leave. If you'd like us to stay on to help, that's an option, not a requirement.

Building intelligent digital products across AI, security, and cloud.

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+966 50 236 8268

info@bmi.com