LLM applications
RAG pipelines, agents, tool-use, evaluation harnesses and the guardrails that make this production-grade.
AI you can actually defend.
Production AI engineering. Retrieval, agents, evaluation and human-in-the-loop controls. For workloads where the answer must be auditable.
Six things AI & Machine Learning usually means in our engagements.
Each capability is shipped by the same team that runs the engagement end-to-end. No handoffs to a different shop mid-way.
Custom models
Classical ML, fine-tuned models and the training pipelines for problems generic LLMs can't solve.
MLOps
Feature stores, model registry, experiment tracking and the CI/CD for ML that nobody talks about.
Evaluation & safety
Eval frameworks, red-teaming, drift detection and the policy layer your compliance team will sign.
Human-in-the-loop
Labelling, review queues and the human-in-the-loop workflows that keep your model honest.
Deployment & inference
Self-hosted, hosted or hybrid inference, with the latency and cost profile that fits your use case.
The default stack for this practice.
Components the bench already runs to production depth, the hiring market already supplies and the customer's security team already approves.
OpenAI
Anthropic
LangChain
LlamaIndex
Pinecone
Weights & Biases
MLflow
NVIDIA Triton
Four phases. Same shape, every engagement.
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01
Discovery & alignment
Stakeholder workshops, real requirements, security review and the kind of spec your team will actually sign.
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02
Architecture & blueprint
Tech selection, infrastructure design, integration mapping, compliance review. The decisions that come before code.
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03
Engineering & ship
Sprints with your team in the room. Security hardening, integration, testing, demos every two weeks.
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04
Operate & evolve
24/7 operations, optimisation and the long-tail engineering that keeps the system running as the business evolves.
Four reasons it usually comes down to.
01
Engineers who have shipped at scale in production AI at enterprise scale. Not consultants reading the manual.
02
Same team from kick-off to year three. No handoff to a different shop after go-live.
03
Audited code, open architecture and the security review your CISO will accept on week one.
04
Fixed-scope or T&M. Whichever way the work needs to be priced.
AI & Machine Learning. The questions buyers ask first.
Short, specific answers from the team that delivers this practice.
What does ASMUTEK AI & Machine Learning actually deliver?
Production AI engineering. Retrieval, agents, evaluation and human-in-the-loop controls. For workloads where the answer must be auditable. Delivered by a senior practice with twelve years of enterprise reference accounts in healthcare, banking, education, manufacturing, telecom and the public sector.
How is ASMUTEK's AI & Machine Learning practice different from a typical agency or system integrator?
Three operating differences. First, the team that scopes the work is the team that runs it through the first two operating cycles. Second, every system we ship is built to pass an independent architecture and security review. Third, customers receive read access to source, runbooks and the deployment topology on signature.
Which technology stack do you use for AI & Machine Learning?
We default to the components our customers' security teams already approve and their hiring market already supplies. The "stack" section of this page lists what we currently run in production. New components only enter customer estates after they have earned a place inside ASMUTEK production first.
How long does a typical AI & Machine Learning engagement take?
The "engagement model" section on this page sets the standard arc. Most ai & machine learning engagements run discovery in two to four weeks, architecture and pilot in four to eight weeks, and full rollout over months three through six. Long-tail run and renewal start at month six and continue across operating cycles.
Which industries do you deliver AI & Machine Learning into most often?
Healthcare, banking and financial services, education, manufacturing, telecom, the public sector and the commodities desks. Sub-sector concentration varies by year but our reference list spans 490 customer environments across 20 countries. Industry-specific references are released on request under NDA.
How is AI & Machine Learning priced?
Two commercial models. Fixed-scope for engagements where the outcome is well-defined and the customer wants budget certainty. Time and materials for ongoing build, run or renewal cells. Both models carry an SLA, named delivery lead and a renewal clause inside the contract. Detailed commercial pack available from the contact desk.
Do you sign customer DPAs and NDAs?
Yes. ASMUTEK signs customer Data Processing Addenda aligned to GDPR Article 28, the UAE Personal Data Protection Law and the DPDP Act 2023. Mutual NDAs are standard. SOC 2 Type II, ISO 27001 certificates and the latest pen-test summary are released through the trust centre under NDA.
Bring us your ai & machine learning problem
Tell us what you're building.
Send a brief, a Loom, or a calendar slot. We'll bring an architect to the first call and a sample of similar work to the second.
- SOC 2 Type II · ISO 27001
- Engineer-led discovery call
- Fixed-scope or T&M