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