Engineering Intelligent Outcomes

Q: What AI/ML services do you offer?

We design and deploy end-to-end AI & Machine Learning solutions: predictive modeling, LLMs (OpenAI/Llama), RAG chatbots, computer vision, recommendation systems, and ML-powered automation. We also cover data engineering, feature stores, evaluation, and MLOps so models run reliably in production.

Q: How do you price AI projects?

We provide fixed-scope pricing for well-defined deliverables or monthly retainers for ongoing AI development and support. Pricing depends on data complexity, model class, integration surface, and compliance.

Q: Typical timeline for PoC to production?

A PoC usually takes 4–6 weeks. Hardening for production with CI/CD, MLOps pipelines, observability, and security typically takes 3–6 months depending on stack and integrations.

Q: How do you protect data & meet GDPR?

We use least-privilege access, encryption in transit and at rest, environment isolation, and audit logging. We follow GDPR best practices and can deploy to your VPC or private cloud. PII minimization, redaction, and input filtering are standard in LLM workloads.

Q: Who owns the models, code & IP?

You own 100% of deliverables upon final payment—source code, model artifacts, data pipelines, infra-as-code, and documentation. Open-weight and closed-weight licensing is clarified up-front.

Q: Do you integrate LLMs, RAG & agents?

Yes. We build domain-tuned LLM applications with RAG (vector and hybrid retrieval), function calling, guardrails, and human-in-the-loop. We select providers based on latency, cost, privacy, and use-case fit.

Q: How do you handle MLOps & scaling?

We implement CI/CD for ML, automated training, model registry, feature store, and canary/blue-green deployments. Serving uses GPU/CPU autoscaling, A/B testing, and cost/latency controls on AWS/Azure/GCP or on-prem Kubernetes.

Q: Do you provide monitoring & support?

Yes. We provide post-launch SLAs with model monitoring (drift, data quality, and safety), alerting, and regular retraining/evaluations to maintain accuracy and ROI.

We build practical AI and ML solutions that automate, predict, and optimize business performance.

Predictive Modeling

NLP & GenAI Agents

Computer Vision

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The DevDot Difference

Production-ready AI that automates, predicts, and personalizes for measurable business outcomes.

Measurable Outcomes

Aligning Models with KPIs

We don't just build models; we build revenue-drivers. We align every project with your core KPIs — from AOV and churn rates to CSAT and SLA times.

This ensures that impact is quantifiable, transparent, and directly tied to your business performance.

Human-Centric AI

Explainable & Accountable

We build explainable AI that keeps humans in control. Every model includes built-in feedback loops, ethical guardrails, and transparent logic.

This approach builds trust, ensures accountability, and empowers your team to make decisions with confidence.

Real-World Ready

Optimized for Production

A model on a laptop is not a product. We optimize for latency, scale, and cost from day one.

Using MLOps pipelines and cloud-native deployment, we deliver production-grade reliability that scales with your enterprise needs.

Our AI Packages

Choose a fixed-scope package or a custom engagement.

Predictive Suite

Demand & sales forecasts
Propensity / churn models
Anomaly & fraud detection

PyTorchXGBoostProphet

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NLP & Agents

RAG chatbots
Summarization & Q&A
Ticket triage & routing

OpenAILlamaLangChain

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Vision & GenAI

Image/video detection
Quality inspection
Gen text/image workflows

CVDiffusionLLM

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Impact Across Industries

A few examples from retail, finance, and operations.

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Personalized ranking systems that boost average order value (AOV) and customer retention. By using adaptive learning and real-time behavior modeling, we turn browsing data into conversions.

Our Core Commitments

The unwavering principles that guide every project we undertake.

Security & Compliance

Every project is built with best practices for data privacy and access control, ensuring your systems are secure by design.

Scalability

We design future-proof solutions engineered for growth and evolving workloads, keeping you ahead of the curve.

Transparency & Ownership

You maintain full ownership and complete visibility of your systems. We believe in transparent partnership and knowledge transfer.

Our Agile AI Process

A predictable path from idea to impact.

01 — Discover & Define

We begin with collaborative workshops to define clear business goals, data constraints, and exact success metrics. We establish the "what" and "why" before the "how."
02 — Architect & Design

A scalable system architecture, data model, and MLOps pipeline are designed. We create a clear delivery roadmap, ensuring our solution is built for the future.
03 — Build & Validate

We build in agile sprints with weekly live demos. Models are continuously validated against your metrics and real-world data to ensure progress and precision.
04 — Launch & Handover

The final solution is securely deployed to your infrastructure. We conduct a full handover with documentation and team training, ensuring you are empowered to own and operate.

Ready to Build Your Competitive Edge?

An idea is just the beginning. Let’s turn your vision into a real, data-driven AI solution securely and at scale.

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AI & ML Outcomes

From proof-of-concept to reliable model impact.

↑18–42%

Conversion/uplift across deployed use cases

↓60%

Labeling & inference cost via distillation/RAG

≤7 days

From spec to working POC

↑4×

Experiment velocity with eval harness

Engagement Models

Choose the path that fits your timeline and risk profile.

Fixed-Scope Packages

LLM/RAG POC, eval suite, guardrails & safety checks.

Typical: 2–6 weeks

Sprint-Based

Feature engineering, offline/online evals, A/B launch.

Typical: 1–3 months

Dedicated Squad

Model lifecycle (drift, retraining), SLA’d inference & ops.

Typical: 3–12 months

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Your AI & Machine Learning Questions, Answered.

Clear, practical details about our AI development, MLOps, data privacy, timelines, pricing, and post-launch support.

What AI/ML services do you offer?

We design and deploy end-to-end AI & Machine Learning solutions: predictive modeling, LLMs (OpenAI/Llama), RAG chatbots, computer vision, recommendation systems, and ML-powered automation. Our team also covers data engineering, feature stores, evaluation, and MLOps so models run reliably in production.

How do you price AI projects?

After a free discovery, we share a transparent proposal with fixed-scope pricing for well-defined deliverables (e.g., a RAG assistant or forecasting model) or monthly retainers for ongoing AI development, experiments, and support. Pricing factors include data complexity, model class, integration surface, and compliance.

Typical timeline for PoC to production?

A PoC usually takes 4–6 weeks focused on feasibility, offline metrics, and a demo. Hardening for production—CI/CD, MLOps pipelines, observability, and security—typically runs 3–6 months depending on the stack and integrations.

How do you protect data & meet GDPR?

We operate with least-privilege access (RBAC), encryption in transit & at rest, environment isolation, and audit logging. We follow GDPR/HIPAA-like best practices where applicable and can deploy to your VPC or private cloud. PII minimization, redaction, and prompt/input filtering are standard in LLM workloads.

Who owns the models, code & IP?

You own 100% of deliverables upon final payment—source code, model artifacts, data pipelines, infra-as-code, and documentation. We support open-weight and closed-weight models with licensing clarified up-front.

Do you integrate LLMs, RAG & agents?

Yes. We build domain-tuned LLM apps with RAG (vector search, hybrid retrieval), function calling/tools, guardrails, and human-in-the-loop. Providers include OpenAI, Anthropic, and open-source Llama stacks; we choose based on latency, cost, privacy, and use-case fit.

How do you handle MLOps & scaling?

We implement CI/CD for ML, automated training, model registry, feature store, and canary/blue-green deployments. Serving uses GPU/CPU autoscaling, A/B testing, and cost/latency controls. Cloud options: AWS/Azure/GCP or on-prem with Kubernetes.

Do you provide monitoring & support?

Yes. We offer post-launch SLAs with model monitoring (drift, data quality, safety), alerting, and regular retraining/evals. Our retainers keep accuracy and ROI strong as data and requirements evolve.

Let's Build The Future, Together.

Have a project in mind or just want to explore possibilities? Drop us a line. We provide a no-obligation proposal with a clear timeline and transparent pricing.

Get in Touch Directly

Email Us
info@devdot.tech
Location

Lahore, Pakistan

Engineering Intelligent Outcomes

The DevDot Difference

Measurable Outcomes

Aligning Models with KPIs

Human-Centric AI

Explainable & Accountable

Real-World Ready

Optimized for Production

Our AI Packages

Predictive Suite

NLP & Agents

Vision & GenAI

Impact Across Industries

Smart Recommenders

Real-time Risk & Fraud Detection

Automated Support & Logistics

Our Core Commitments

Security & Compliance

Scalability

Transparency & Ownership

Our Agile AI Process

01 — Discover & Define

02 — Architect & Design

03 — Build & Validate

04 — Launch & Handover

Ready to Build Your Competitive Edge?

AI & ML Outcomes

↑18–42%

↓60%

≤7 days

↑4×

Engagement Models

Fixed-Scope Packages

Sprint-Based

Dedicated Squad

Let's Build The Future, Together.

Get in Touch Directly

Email Us

Location

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