AI Solutions

Services

Generative AI Product Design

We shape ideas into clear, feasible products that deliver value to our customers.

In practice

• Define target users, core jobs to be done, and success metrics.

• Select model options (for example, hosted foundation model, fine-tuned model, or retrieval-augmented approach) based on data sensitivity, latency, and cost.

• Draft conversation flows and interaction patterns for chat, summarization, extraction, content drafting, and decision support.

Example
A customer support portal adds a guided assistant that drafts accurate responses from existing knowledge articles and past tickets, reducing agent handling time by thirty percent.

Retrieval-Augmented Generation (RAG)

We connect models to your private knowledge safely and reliably.

In practice

• Build secure connectors to document stores, data warehouses, and application programming interfaces.

• Design chunking, embedding, and indexing strategies that preserve context and prevent leakage.

• Introduce guardrails that restrict retrieval to the right collections and enforce user permissions.

Example
A professional services firm enables consultants to query proposals, project notes, and legal clauses. The assistant cites sources and respects document access rights.

AI Agents and Orchestration

We design semi-autonomous agents that can plan tasks, call tools, and work together.

In practice

• Break complex workflows into agent roles such as planner, researcher, writer, reviewer, and publisher.

• Integrate tools for search, databases, spreadsheets, code execution, and ticketing systems.

• Add human-in-the-loop checkpoints for sensitive steps and policy compliance.

Example
A marketing team uses a campaign agent that drafts a plan, gathers product facts, writes first versions of assets, requests human review, and schedules approved items.

Prompt Engineering and Evaluation

We create robust prompts and templates, then measure quality with automated tests and human review.

In practice

• Design reusable prompt patterns and system instructions for different tasks and tones.

• Build golden datasets with expected outputs to run automated evaluations.

• Compare versions with side-by-side review and collect structured feedback.

Example
A knowledge assistant improves answer accuracy by establishing a test set of one hundred real questions and running a weekly evaluation when content changes.

Safety, Policy Controls, and Compliance

We implement safety measures and governance so that teams can innovate with confidence.

In practice

• Configure content filters for toxicity, personal data, confidential terms, and hallucination risk.

• Add approval workflows, usage policies, and retention rules for prompts, context, and outputs.

• Produce explainability summaries and audit trails for internal and regulatory reviews.

Example
A health care provider blocks prohibited content, masks personal data in logs, and keeps a complete history of prompts and responses for audit purposes.

Operations at Scale (GenAI Operations)

We run generative systems with the same discipline used for critical software.

In practice

• Instrument cost, latency, quality, and safety metrics; set alerts and budgets.

• Introduce release strategies for prompts and chains with rollback and version control.

• Optimize model selection, caching, batch processing, and autoscaling.

Example
An e-commerce assistant keeps average response time under two seconds during peak traffic by caching frequent answers and routing tasks to the most efficient model.

DELIVERABLES

ENGAGEMENT EXAMPLES

Machine Learning Solutions

Services

Strategy and Use-Case Discovery

We help you identify high-value opportunities where Machine Learning can create measurable impact. Together we define success criteria, technical feasibility, and a clear path to production.

In practice

• Map business objectives to candidate use cases such as demand forecasting, churn prediction, fraud detection, and recommendation systems.

• Score use cases across expected impact, data readiness, complexity, and time to value.

• Select a pilot that balances risk and reward, for example a lead-scoring model that routes sales efforts toward the most promising prospects.

Example
A services company with many inbound leads reduces response time and increases conversion by prioritizing the top twenty percent of leads based on predicted win probability.

Data and Feature Engineering

We design and build robust data pipelines that are the foundation for reliable Machine Learning. This includes ingestion, validation, transformation, and feature creation.

In practice

• Connect to operational systems and data warehouses; standardize formats and enforce data quality checks.

• Create features that improve predictive power, for example lagged metrics, rolling windows, seasonality indicators, and text embeddings.

• Establish a feature catalog with clear ownership and documentation so that teams can reuse high-quality features.

Example
For a subscription platform, we build a daily pipeline that aggregates login frequency, product usage depth, and support ticket sentiment into features that drive a churn prediction model.

Model Development and Validation

We design, train, and evaluate models that are fit for purpose and easy to maintain.

In practice

• Start with strong baselines and progressively evaluate more sophisticated methods when they are justified by results.

• Use rigorous validation that mirrors real-world conditions, including time-based splits for time series forecasting and stratified splits for imbalanced classification.

• Document performance and limitations in clear language for both technical and non-technical stakeholders.

Example
A retailer’s demand forecast improves replenishment planning by combining gradient-boosted trees with seasonality features, reducing stock-outs without increasing inventory.

Operationalization and Lifecycle Management

We make Machine Learning part of your day-to-day operations, not an experimental island.

In practice

• Package models for consistent deployment to your preferred environment, such as a container platform or a serverless endpoint.

• Automate training, evaluation, and deployment with quality gates. A model only progresses when it meets agreed thresholds for accuracy, fairness, and latency.

• Monitor model behavior in production, including data drift, prediction quality, and service availability, and define playbooks for rollback and retraining.

Example
An insurance provider deploys a pricing model with automatic hourly monitoring of input drift. When drift is detected, the system triggers an alert, schedules a retraining job, and requires human approval before promotion.

Performance, Cost, and Reliability Optimization

We help you reach service-level objectives while controlling operational costs.

In practice

• Profile end-to-end latency and remove bottlenecks in feature generation, model inference, and data access.

• Right-size compute for training and serving; use techniques such as model quantization and efficient feature stores to reduce costs.

• Build graceful degradation strategies so that critical journeys continue to work even during upstream incidents.

Example
A real-time risk scoring service reduces median response time from 450 milliseconds to 120 milliseconds by caching features and simplifying the feature pipeline for online requests.

Governance, Security, and Compliance

We build Machine Learning with trust and accountability from the start.

In practice

• Implement access controls, encryption, and key management for data and models.

• Provide explainability reports so that product owners and compliance teams can understand the drivers behind predictions.

• Maintain auditable records of datasets, training runs, approvals, and deployments.

Example
A financial institution launches a credit decision support model with clear explanations of factor contributions on each decision, enabling fair-lending reviews and faster sign-off.

DELIVERABLES

ENGAGEMENT EXAMPLES