P1 · Agents
Agentic Systems & Orchestration
Planner and executor architectures, typed tool contracts and deterministic control planes for multi-step enterprise workflows with human checkpoints.
From frontier modelsto governed systems.
The Magicwork AI Lab is our applied research and platform unit. It converts advances in foundation models, retrieval, multimodal perception and decision science into reference architectures, evaluation harnesses and reusable components, hardened for latency, cost, security and regulatory scrutiny before they reach a client estate.
01Charter
The AI Lab exists to close the gap between what foundation models can demonstrate and what an enterprise can defend in production, converting frontier capability into systems that are evaluated, observable, secure and economically rational before a single decision is automated.
8Research programmes spanning agents, retrieval, perception, language and decision science
6Layers in our enterprise AI reference architecture, governed end to end
100%Of model, prompt and retrieval changes gated on regression evaluation
IN · AESovereign deployment topologies for India and the United Arab Emirates
Perception · Pattern · Meaning
Intelligence is pattern,made visible.
Van Gogh painted turbulence decades before physics could describe it, and researchers have since found that his swirls follow the statistics of real turbulence. Our research starts the same way: find the structure in the noise, then prove that it holds.
02Research programmes
Eight programmes.One production standard.
Each programme maintains its own benchmark suite, reference implementation and cost model, and graduates components into client delivery only when they clear the same evaluation gates we apply in production.
P2 · Retrieval
Retrieval & Knowledge Engineering
Hybrid lexical and dense retrieval, cross-encoder re-ranking, GraphRAG and entitlement-aware access over heterogeneous enterprise corpora.
P3 · Perception
Document & Vision Intelligence
Layout-aware extraction, vision-language models and field-level verification for claims, KYC, invoices, contracts and clinical records.
P4 · Language
Speech & Language: Indic and Arabic
Code-mixed ASR, multilingual dialogue management and neural TTS across India's major languages, Modern Standard Arabic and Gulf dialects.
P5 · Decisions
Forecasting & Decision Science
Probabilistic forecasting, causal inference and mathematical optimisation that convert predictions into decisions with quantified uncertainty.
P6 · Assurance
Evaluation, Safety & Governance
Golden datasets, judge-model calibration against human raters, adversarial red-teaming and policy-as-code guardrails with audit trails.
P7 · Edge
Edge AI & Computer Vision
Quantised detection, recognition and inspection models for ANPR, occupancy and quality control on constrained edge hardware.
P8 · Infrastructure
AI Infrastructure & LLMOps
Model routing, semantic caching, high-throughput serving and per-transaction cost telemetry across frontier APIs and self-hosted open-weight models.
03Reference architecture
Six layers.Governed as one.
Our enterprise AI reference architecture separates concerns so each layer can evolve independently: models are swapped without rewriting workflows, and governance applies uniformly across every layer rather than being bolted onto the last one.
- L6
- Experience & channelsWeb, mobile, WhatsApp, voice and API surfaces with session-aware context.
- L5
- OrchestrationAgents, durable workflows, typed tool contracts and human approval gates.
- L4
- IntelligenceFrontier LLMs, fine-tuned small models and classical ML behind a routing layer.
- L3
- KnowledgeHybrid search, knowledge graphs and feature stores with entitlement filtering.
- L2
- DataLakehouse, CDC streams and data contracts with lineage to source.
- L1
- InfrastructureKubernetes, GPU pools, multi-cloud and on-premises, in-region by design.
04Research to production
A lifecycle designedfor audit, not applause.
Every AI capability moves through six gated stages. Each gate produces an artefact that a risk committee, an auditor or a regulator can read, and each can send the work back.
01
Frame
Decision definition, baseline economics and value hypothesis, with risk tiering of the use case.
Use-case canvas · risk tier02
Data
Data audit, access design, PII treatment and construction of golden evaluation datasets.
Data contract · eval set03
Prototype
Architecture spikes across model classes, retrieval strategies and tool-use patterns.
Spike report · ADRs04
Evaluate
Offline evaluation, red-teaming, human preference review and unit-cost modelling.
Eval report · cost model05
Harden
Guardrails, observability, threat modelling, load testing and failure-mode design.
Threat model · runbooks06
Operate
Canary release, online evaluation, drift monitoring and a continuous-improvement loop.
SLOs · model card
05Model strategy
Model-agnosticby architecture.
We place each task on the frontier of accuracy, latency and cost, and route it to the smallest model that clears its evaluation bar, preserving the freedom to adopt better models as the market moves.
Reasoning-intensive
Frontier model APIs
Complex reasoning, drafting and multimodal understanding through enterprise agreements with zero-retention options, regional endpoints and a routing layer that prevents vendor lock-in.
Sovereign · high-volume
Open-weight & fine-tuned SLMs
Self-hosted open-weight and task-specific small language models, distilled, quantised and served inside your VPC or data centre for latency-sensitive and data-sovereign workloads.
Deterministic
Classical ML & optimisation
Gradient-boosted models, statistical forecasting and mathematical optimisation wherever decisions must be explainable, stable and inexpensive to run at scale.
P4 · Speech & language
Engineered for how India and the Gulf actually communicate.
Code-mixed speech, regional scripts, transliteration and dialectal Arabic, evaluated on in-domain audio and text rather than translated benchmarks, and deployable on sovereign infrastructure.
- Hindiहिन्दी
- Marathiमराठी
- Gujaratiગુજરાતી
- Bengaliবাংলা
- Tamilதமிழ்
- Teluguతెలుగు
- Kannadaಕನ್ನಡ
- Malayalamമലയാളം
- Punjabiਪੰਜਾਬੀ
- Odiaଓଡ଼ିଆ
- Urduاردو
- Arabicالعربية
Observability · Evidence · Trust
Measure whatyou cannot see.
A black hole is never observed directly; it is known by the light bending around it. Model behaviour is similar, so we make it visible through evaluation sets, traces and telemetry before anyone is asked to trust it.
06Governance
Responsible AI,implemented as engineering.
Governance is expressed as code, tests and telemetry, not as a policy document. Our controls are designed in line with the Digital Personal Data Protection Act, 2023, the UAE Personal Data Protection Law, the NIST AI Risk Management Framework and ISO/IEC 42001 principles.
Evaluation & red-teaming
Golden datasets, adversarial suites and judge models calibrated against human raters, with regression gates on every model, prompt and retrieval change.
Guardrails as code
Input and output policies, PII redaction, prompt-injection defences and tool-permission scopes versioned alongside application code.
Privacy & sovereignty
Data minimisation, purpose limitation, consent-aware pipelines and in-region processing for Indian and UAE data subjects.
Observability & lineage
Trace-level capture of prompts, retrieved context, tool calls and outputs, with lineage from source document to generated answer and cost per transaction.
Human authority
Maker-checker workflows and approval thresholds keep people accountable for decisions affecting money, identity, health and legal standing.
Model risk management
Model cards, risk tiering, change control and periodic validation, so every model in production has an owner, a purpose and an expiry review.
07Engage the Lab
From hypothesisto production economics.
Four entry points, each with a defined artefact at exit, so leadership can fund AI in stages and stop, pivot or scale on evidence.
01 · 2 to 4 weeks
AI opportunity diagnostic
Use-case discovery, value-at-stake sizing, data readiness and risk tiering, prioritised into an AI roadmap with a business case per initiative.
02 · 6 to 8 weeks
Proof of value
A production-shaped pilot on your data, closing with an evaluation report, a unit-cost model and a go / no-go recommendation.
03 · Quarterly increments
Production programme
Integration, guardrails, observability and change management delivered in governed increments against agreed service levels.
04 · Run
Managed LLMOps
Model routing, continuous evaluation, monitoring, cost optimisation and model upgrades operated as a managed service.
AI Lab · India & UAE
Put a hypothesis
Put a hypothesis
under evaluation.
Bring a use case and a dataset. Within weeks you will know what it is worth, what it costs to run and what it takes to govern.