Everything You Need
to Know.

We offer five core AI engineering specialisms — available individually or as a combined engagement:

• LLM Integration & Fine-Tuning — GPT-4, LLaMA 3, Grok-2, custom RLHF and DPO pipelines, enterprise chatbots
• RAG Systems & Knowledge Bases — Retrieval-augmented generation over 500K+ documents, FAISS, Pinecone, hybrid search, sub-second latency
• Real-Time AI & Fraud Detection — Graph neural networks, sub-50ms transaction scoring, Kafka + Redis pipelines, core banking integration
• Computer Vision — YOLOv8 defect detection, NVIDIA Jetson edge deployment, TensorRT optimization, 99%+ accuracy at 18ms inference
• MLOps & AI Infrastructure — End-to-end pipelines on Azure ML, AWS SageMaker, Kubernetes, 99.95% uptime SLA, drift monitoring
• Multilingual NLP — Urdu, Arabic, English sentiment analysis using XLM-RoBERTa, 100K+ entries/day, BERTopic topic discovery

Three things that aren't common:

• You work directly with the founder — Muhammad Awais is an AI Project Managing Director who has shipped $10M+ AI portfolios at xAI. No handoff to junior staff.
• Everything is production-grade — we don't do demos or PoCs that gather dust. Every deliverable is designed to run in live systems under real load.
• Frontier-lab context — having worked at xAI (Grok LLMs), the engineering bar here is genuinely higher than most consultancies operating in this space.

Both. Our Basic and Medium tiers are specifically designed for startups and growing companies who want real AI — not toy demos — without enterprise-scale budgets. We have worked with seed-stage startups, mid-market SaaS companies, and large financial institutions.

The honest starting point is: if you have a data problem and a production environment, we can probably help you.

It's a retainer arrangement where Muhammad Awais acts as your part-time AI Director. This is ideal for companies who need senior AI strategy and technical leadership but aren't ready to hire a full-time CDO or VP of AI.

• AI roadmap and strategy development
• Technical due diligence on vendors and models
• Team mentoring and upskilling
• Board-level AI reporting and governance
• Weekly strategy calls, async support

Available under the Supreme and Enterprise tiers.

We have five fixed-scope tiers, each with defined deliverables and pricing — no surprise invoices:

• Basic — $2,500 / project. AI readiness assessment, 1 model integration, 2-week delivery.
• Medium — $6,500 / project. Custom fine-tuning or RAG system, MLOps setup, 4-week delivery.
• Premium — $12,000 / project. Multi-system integration, real-time components, 6-week delivery.
• Supreme — $22,000 / project. Full AI product build, RLHF, compliance auditing, 3-month engagement.
• Enterprise — $40,000+. Custom architecture, dedicated engineer, 6-month engagement, SLA guarantees.

We accept bank wire transfer, PayPal, and USDT/crypto. For Enterprise engagements, we issue formal invoices in USD and can accommodate most corporate payment processes including net-30 and net-60 terms.

Basic and Medium tiers are typically 50% upfront, 50% on delivery. Enterprise engagements are structured with milestone-based payments so you only pay as work is completed.

Yes. Two retainer options:

• Post-project support retainer — starting at $800/month for monitoring, bug fixes, minor updates, and drift alerting on deployed models.
• Fractional AI Director retainer — $5,000/month for ongoing strategic leadership, available under Supreme and Enterprise tiers.

Retainers are month-to-month with 30-day notice to terminate — no lock-in.

Correct. For Enterprise-tier engagements, the first step is always a free scoping call and a written proposal. We don't invoice for discovery or architecture planning. The first milestone payment is triggered only when you formally approve the project scope in writing.

This is standard for serious enterprise relationships — a CTO shouldn't have to pay to find out if a vendor is the right fit.

We don't offer a blanket refund policy — AI engineering is a complex, custom service. What we do offer instead:

• Clearly defined deliverables in the contract before any work starts
• Milestone-based payments for Premium and above — you approve each phase before paying for the next
• Revision rounds (1–unlimited depending on tier) until deliverables meet agreed specs
• If we fail to deliver the agreed spec, we work until it's correct at no extra charge

Fill out the contact form or email directly. We respond within 24 hours with a brief questionnaire about your data, problem, and timeline. Then we schedule a 30-minute discovery call — free, no commitment. After that call you'll receive a written scope proposal within 48 hours.

Delivery timelines depend on scope:

• Basic — 2 weeks
• Medium — 3–4 weeks
• Premium — 5–6 weeks
• Supreme — 8–12 weeks (3 months)
• Enterprise — 3–6 months

These are based on us having access to your data and your team for reviews at agreed checkpoints. Delays on your end extend timelines proportionally.

Yes — NDA on day 1, before any discussion of your data or architecture. We use a standard mutual NDA and can work with your legal team's preferred template if you have one.

Your data never touches third-party training pipelines without explicit written consent. When we deploy to your cloud environment (Azure, AWS, GCP), the data stays in your infrastructure — we don't exfiltrate or retain it.

For every project, you get a shared Notion workspace (or your preferred tool) with the project spec, timeline, and milestone tracker. Communication is through your preferred channel — email, Slack, WhatsApp, or Teams.

• Basic/Medium: weekly written status update + milestone demo
• Premium/Supreme: bi-weekly video sync + async updates 3x/week
• Enterprise: weekly video call + daily async standups in your Slack

You do. Full intellectual property transfer is included in every engagement. Upon final payment, you own all code, models, weights, pipelines, and documentation. We retain no rights to your system, data, or architecture. This is stated explicitly in our contract, not just implied.

It depends on the service:

• LLM API integrations — no training data needed, just API access and your use-case spec
• Fine-tuning / RLHF — ideally 500–10K labelled examples in your domain; we can advise on data collection if needed
• RAG systems — your document corpus (PDFs, Word docs, databases) in any format
• Fraud / anomaly detection — historical transaction data with labelled fraud events (minimum 6 months, ideally 2+ years)
• Computer Vision — labelled image dataset; we can advise on minimum sample size for your accuracy target

If you have no data yet, we can help you design a data collection and labelling strategy as part of the Basic or Medium tier.

We are cloud-agnostic and have production experience on all three major platforms:

• Azure — Azure ML, Azure OpenAI, AKS, Cosmos DB, Azure Functions
• AWS — SageMaker, Lambda, EKS, DynamoDB, Bedrock
• GCP — Vertex AI, Cloud Run, BigQuery ML

We can also deploy on-premise or in a hybrid setup. If you have existing infrastructure, we work within it rather than forcing a migration.

Primary language is Python. Key frameworks and tools by domain:

• LLMs & NLP — PyTorch, HuggingFace Transformers, LangChain, LlamaIndex, OpenAI SDK, vLLM
• Computer Vision — Ultralytics YOLOv8, TensorRT, ONNX Runtime, OpenCV, timm
• Vector Search — FAISS, Pinecone, Qdrant, Weaviate
• MLOps — MLflow, Kubernetes, Docker, GitHub Actions, Azure ML Pipelines
• Streaming / Real-Time — Apache Kafka, Redis Streams, FastAPI, Celery
• Data — Spark, Databricks, dbt, Pandas, Polars

We agree on target metrics upfront (accuracy, precision, recall, latency, throughput) as part of the project contract, and those targets are what we're held to. Whether we can guarantee a specific number depends on your data quality and the complexity of the problem.

We are always transparent in the scoping call if a target is realistically achievable. We don't promise 99% accuracy on a 500-sample dataset — but we'll tell you that clearly before you sign anything.

Integration is usually preferred over greenfield builds — it's cheaper, faster, and avoids migration risk. We regularly integrate with:

• Existing REST/GraphQL APIs
• Core banking and ERP systems
• CRM and customer data platforms
• Data warehouses (Snowflake, BigQuery, Redshift)
• Document management systems (SharePoint, Confluence, S3)

We review your existing architecture in the scoping call and recommend the lowest-risk path to production.

For Basic and Medium engagements, Muhammad Awais leads and personally builds the core system. For Premium, Supreme, and Enterprise projects, he brings in trusted specialist collaborators (data engineers, DevOps, frontend) as needed — but he remains the technical lead and your point of contact throughout.

There is no bait-and-switch where you scope with a senior and get handed to a junior team.

In practice, it hasn't been a problem. Our clients have been based in the UK, USA, UAE, and Germany. Pakistan Standard Time (PKT, UTC+5) overlaps well with UK morning hours and has a 5–9 hour offset from US Eastern time — enough overlap for a daily or weekly sync call.

For Enterprise clients who need near-real-time availability, we can arrange partial timezone overlap schedules. We've led 35+ person research teams across distributed timezones before — async-first communication is how serious engineering gets done.

Every tier includes a support window after launch:

• Basic — 30 days bug-fix support
• Medium — 45 days bug-fix + minor update support
• Premium — 60 days full support including performance tuning
• Supreme / Enterprise — 90 days, then optional retainer for ongoing MLOps

After the included support window, we offer a monthly maintenance retainer starting at $800/month that covers drift monitoring, model updates, and system health.

All technical work, documentation, and client communication is conducted in English. For clients based in Pakistan, the UAE, or South Asia who prefer Urdu for informal discussions, that's also fine — but all deliverables (code, docs, reports) are in English regardless.

Yes. The About & Portfolio page has eight production AI systems with impact metrics. Detailed case studies (architecture diagrams, technical approach, measurable outcomes) are available under NDA for serious prospects — we share them on the discovery call.

Reference calls with previous clients can be arranged for Enterprise engagements. Due to NDA obligations we cannot proactively name clients publicly, but with their permission we can facilitate introductions.