AI, Machine Learning & Generative AI

From predictive ML models to production RAG systems, agentic workflows, and natural-language querying - we build AI that works in the real world, governed by engineering rigor.

AI Practice Areas

Local + Cloud

LLM Deployment Models

Millions

Data Points Processed

Overview

We build production-grade AI systems across classical ML, generative AI, and agentic workflows - with a strong emphasis on data privacy, governance, and enterprise integration. Our engineering approach combines predictive models (credit risk, forecasting, anomaly detection) with modern LLM capabilities (RAG, natural language querying, agentic automation) and hybrid deployment strategies using both local and cloud-hosted LLMs. Every AI system we build is designed for real-world production use - with observability, guardrails, human-in-the-loop controls, and architectural rigor as foundational principles.

Our Process

Data Assessment

Evaluate data quality, availability, and readiness. Identify gaps, biases, and collection requirements for the ML objective.

Feature Engineering

Transform raw data into meaningful features. Domain-specific feature design that captures the signals your model needs.

Model Training & Selection

Train multiple model architectures, tune hyperparameters, and select the best performer using rigorous cross-validation.

Validation & Testing

Out-of-sample testing, fairness audits, and business metric validation. We don't ship models that only work on training data.

Deployment & Integration

Production deployment with REST API endpoints, batch processing pipelines, and integration with your existing systems.

Monitoring & Iteration

Continuous model performance monitoring, drift detection, automated retraining triggers, and accuracy reporting.

Data Assessment

Evaluate data quality, availability, and readiness. Identify gaps, biases, and collection requirements for the ML objective.

Feature Engineering

Transform raw data into meaningful features. Domain-specific feature design that captures the signals your model needs.

Model Training & Selection

Train multiple model architectures, tune hyperparameters, and select the best performer using rigorous cross-validation.

Validation & Testing

Out-of-sample testing, fairness audits, and business metric validation. We don't ship models that only work on training data.

Deployment & Integration

Production deployment with REST API endpoints, batch processing pipelines, and integration with your existing systems.

Monitoring & Iteration

Continuous model performance monitoring, drift detection, automated retraining triggers, and accuracy reporting.

AI PRACTICE AREAS

Six Capabilities That Go Beyond Generic AI

From predictive modeling to agentic AI systems - each practice is grounded in production deployment experience, not proof-of-concept demos.

Practice Area

Predictive ML & Early Warning Systems

Production-grade predictive models for credit risk, loan default prediction, churn forecasting, fraud detection, and operational anomaly detection. Built with gradient boosting (CatBoost, XGBoost), deep learning, and rigorous out-of-sample validation. Deployed models processing portfolios of 22,000+ records across multi-branch financial institutions.

Tech / Focus Areas

CatBoostXGBoostscikit-learnTensorFlowCredit risk modelingFraud detectionForecasting

Practice Area

Natural Language Querying (NLQ)

Convert plain-English business questions into SQL queries, visualizations, and actionable insights - without requiring technical knowledge from end users. We have built NLQ systems that sit on top of enterprise databases, understand schema context, generate correct SQL, and return both raw data and narrative insights. Built for the Quore hospitality platform and adaptable to other enterprise data stacks.

Tech / Focus Areas

Text-to-SQLSchema-aware queryingActionable insights generationLLM-powered analyticsGuardrails

Practice Area

RAG Systems & Knowledge Retrieval

Retrieval-Augmented Generation systems that ground LLM responses in your own enterprise documents, policies, codebases, or knowledge bases. Production deployments using vector databases (Weaviate, pgvector, Pinecone), semantic chunking, hybrid retrieval (vector + keyword), and re-ranking pipelines. Designed to answer the hardest enterprise question: "what does our company know about X?"

Tech / Focus Areas

WeaviatepgvectorPineconeEmbedding modelsHybrid retrievalRe-rankingSemantic search

Practice Area

Agentic AI Systems

Multi-step autonomous workflows where AI agents plan, reason, and execute against enterprise tools and APIs. Built with frameworks like LangGraph, LlamaIndex, and custom orchestration layers. Every agent design includes explicit tool boundaries, human approval checkpoints, audit logging, and failure recovery - because autonomous AI in the enterprise must be governed, not just capable.

Tech / Focus Areas

LangGraphLlamaIndexTool useAgent orchestrationHuman-in-the-loop designAudit logging

Practice Area

Hybrid LLM Deployment (Local + External)

Strategic deployment of both local LLMs (for data privacy, cost control, and offline capability) and external LLMs (OpenAI, Anthropic, Google) when appropriate. We design hybrid architectures where sensitive data stays on-premises with local models (Llama, Mistral, Phi), while non-sensitive tasks leverage frontier cloud models. Includes prompt engineering, caching, fallback strategies, and cost optimization.

Tech / Focus Areas

OllamaLlamaMistralPhiOpenAIAnthropic ClaudePrompt engineeringModel routing

Practice Area

AI-Powered Insights & Automation

Transform raw data into actionable insights through AI-powered analysis, summarization, and workflow automation. Systems that do not just show dashboards - they explain what is happening, why it is happening, and what to do next. Applications include executive briefings, operational reports, customer sentiment analysis, and intelligent document processing.

Tech / Focus Areas

Insight generationAutomated summarizationDocument processingSentiment analysisWorkflow automation

Every AI system we build is human-governed by design.

Our engineering approach mirrors the "AI-Accelerated Engineering. Human-Governed Quality." principle from our homepage - every model, every agent, every prompt is built with explicit human oversight, safety controls, and auditability. AI accelerates outcomes; humans remain accountable.

Key Capabilities

Predictive analytics, forecasting, and early warning systems

Natural Language Querying (text-to-SQL, conversational analytics)

RAG systems with enterprise knowledge bases

Agentic AI workflows with tool use and human-in-the-loop

Hybrid LLM deployment (local + cloud) for data privacy

Intelligent document processing and extraction

AI-powered insights and automated summarization

MLOps and production model lifecycle management

Model governance, observability, and drift detection

Prompt engineering and guardrail design

Vector database architecture and semantic search

AI strategy consulting and use-case evaluation

Technologies

30+ tools, frameworks, and platforms

Classical ML

PythonTensorFlowPyTorchscikit-learnCatBoostXGBoostMLflowAWS SageMaker

LLMs - Cloud

OpenAIAnthropic ClaudeGoogle GeminiAzure OpenAI

LLMs - Local

OllamaLlamaMistralPhiQwenDeepSeek

Agent Frameworks

LangGraphLlamaIndexLangChainCrewAI

Vector Databases

WeaviatepgvectorPineconeQdrantChromaDB

Embedding Models

OpenAI AdaSentence TransformersBGECohere Embed

Data & Infrastructure

PostgreSQLRedisAirflowDockerKubernetes

What You Receive

Trained Production Model

Validated, optimized ML model packaged for production deployment with version control.

MLOps Pipeline

Automated training, validation, and deployment pipeline with monitoring and alerting.

Performance Report

Detailed model evaluation report with accuracy metrics, confusion matrices, and business impact analysis.

Integration API

REST API endpoints for real-time inference with authentication, rate limiting, and documentation.

Monitoring Dashboard

Real-time model performance dashboard tracking predictions, accuracy drift, and data quality.

RAG System Documentation

Embedding strategy, chunking approach, retrieval configuration, and re-ranking logic documented for reproducibility.

Governance & Guardrails Framework

Prompt templates, safety controls, human approval checkpoints, audit logs, and escalation policies for agentic systems.

Related Case Studies

Financial Services

AI/ML Loan Prediction & Early Warning System

A Bangladesh financial institution needed to predict loan defaults before they happened and flag high-risk accounts across a portfolio of 22,000+ loans spanning 14 branches.

PythonFastAPICatBoostXGBoost

Read case study

Let’s Engineer Something Remarkable

Whether you’re modernizing legacy systems, building a new product, or need senior engineering muscle — we’re ready.

Schedule a Discovery Call

View Our Work

hello@periscopelabs.io · +880 9611 656808 · +880 1626 934015 · +1 646 267 9820

Overview