The technology
that creates —
engineered for your business.
Generative AI produces text, code, images, decisions and knowledge on demand. AMDIM engineers it grounded, adapted and governed — so it delivers measurable output, not a demo.
/ what generative ai creates
Five things your business
can now produce on demand
GenAI is the technology category that generates — not a chatbot product, not a single use case. It creates across every format your business runs on.
Content & knowledge
Drafts, summaries, reports and answers generated on demand — grounded in your documents, not the open web.
faster knowledge work
GitHub Research, 2024
Code & developer acceleration
Code completion, review, documentation and test generation — cutting the time from idea to production.
less time debugging
GitHub Research, 2024
Document intelligence
Reads invoices, contracts, forms and reports — extracts structured data at any volume, automatically.
manual extraction eliminated
McKinsey, 2023
Multi-modal creation
Generates and understands images, audio, video and text in a single pipeline — one AI, every format.
media processing speed
AMDIM delivery benchmarks
Domain-adapted models
Fine-tuned models that follow your formats, tone and compliance rules — sharper, cheaper and entirely yours.
lower inference cost vs. GPT-4
Hugging Face benchmarks, 2024
/ the engineering gap
GenAI isn't the problem.
How it's used is.
Every major AI lab ships capable foundation models. The gap between a failed GenAI project and a productive one is entirely engineering — grounding, adaptation, evaluation and governance.
What most deployments look like
/ how we engineer it
Five steps from
foundation model to production
Every AMDIM GenAI engagement runs the same engineering discipline — because a working demo is not a production system.
/ how grounded genai works
Prompt in.
Cited answer out.
Every response retrieves the relevant passage from your knowledge base first — so the model generates from your facts, not its training data. Click any input below to watch the flow.
/ the market signal
GenAI is no longer optional
The organisations building a compounding advantage now are the ones that engineered GenAI properly from the start — not the ones who deployed a chatbot.
annual value GenAI could add to the global economy
McKinsey Global Institute, 2023
faster task completion with an AI copilot
GitHub Research, 2024
of enterprises will use GenAI by 2026
Gartner, 2024
/ the copilot payoff
Hours back.
Every week.
Drag in your team — the payoff computes live, defaulting below GitHub's measured 55% speed-up.
Annual cost of assistable work today
You keep / year
$518.4KHours on assistable work / year
8,640 hrs hrs returnedTeam tied to this work
4.5 FTEs freed50 people saving 30% on 12 hrs/week is worth ≈ $518.4K a year.
/ safe by design
Private, governed,
traceable
Your data never trains someone else's model. Every answer is traceable to a source you own. Governance is not an afterthought — it is built into the engineering from day one.
Regulated industries (finance, healthcare, legal) routinely run our deployments in air-gapped or VPC environments. The compliance team gets an audit trail; the security team gets no public data egress.
Private deployment
On-prem, VPC or air-gapped — your data never reaches a public endpoint.
Source citations on every answer
Retrieval grounding means every output can be traced back to the document it came from.
Guardrails & red-teaming
Llama Guard, NeMo Guardrails and adversarial red-teaming block prompt injection, PII leakage and toxic outputs.
Full audit trail
Langfuse traces every prompt, token and response — role-gated access controls included.
/ the depth
Beyond the basics —
the full toolbox
5 capabilities, each backed by a real toolbox — prompted to adapted / trained. A taste below; the full library runs deep.
techniques · 13 disciplines
Retrieve & ground
answers from your data, with citations
Adapt & specialise
make the model yours
Generate & create
text, documents, images, speech & code
Evaluate & guard
so it never ships wrong or unsafe
Operate
reliable, fast and cost-controlled at scale
/ your industry
The hard problems
that actually pay
Every sector has a handful of problems that are genuinely hard — and genuinely worth it. Pick your industry for a taste; the full set lives on the industries hub.
Manufacturing
why it’s hard — Defects are rare events on fast lines; models run at the edge in real time, with near-zero tolerance for a missed fault.
$50B/yr lost to unplanned downtime (Deloitte); AI-in-manufacturing ~35% CAGR to 2030 (Grand View).
Maintenance copilot grounded in manuals & incident logs
A retrieval-grounded LLM over OEM manuals, SOPs and past incidents gives a technician the likely cause and fix in plain language — with the exact page it came from.
Your best maintenance engineer retires next month, and his fix is in a binder no one reads.
evidenceCopilots + RCA cut MTTR 30–40% in analogous ops (McKinsey)
Generative design & topology optimisation
AI generates lightweight, manufacturable geometries that still meet every load, tolerance and material constraint — options no engineer would draw by hand.
Every gram of material is margin — or waste — repeated across a million parts.
evidenceGenerative design can cut development time/cost ~10× (NASA)
Synthetic defect generation for new-SKU inspection
Generative models synthesise photorealistic defect images so a vision inspector learns a novel fault class from a handful of real examples.
You can't wait ten thousand rejects to teach the inspector what a bad part looks like.
/ why it pays
Outcomes you can measure
faster knowledge work (GitHub)
Knowledge & copilots
Assistants for support, ops and sales grounded in your documents — every answer cited to the source.
content throughput
Content & code generation
On-brand copy, code and summaries produced at scale and reviewed in a fraction of the time.
manual extraction eliminated
Document intelligence
Extract structured data from invoices, contracts and reports automatically — at any volume.
lower inference cost vs. GPT-4
Domain-adapted models
Fine-tuned models that follow your formats, tone and compliance rules — cheaper to run, sharper on your data.
media processing speed
Multi-modal generation
Generate and understand images, audio and video alongside text — one unified AI capability.
grounded answer accuracy
Evaluation & governance
Proof that it works: RAGAS eval suites, red-teaming, guardrails and full audit trail before it faces a customer.
McKinsey Global Institute, 2023 · GitHub Research, 2024 · Gartner, 2024 — verified, not invented.
/ what we deliver
End-to-end, not half-built.
/ before you commit
Fair questions
No. ChatGPT is one consumer product built on top of a foundation model. Generative AI is the entire technology category — foundation models, fine-tuning, RAG, multi-modal generation, code AI and more. We engineer it into your products and workflows, not just give you a chatbot.
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