"DeepCurve AI helped us turn an internal AI prototype into an operating workflow. The system added approvals, traceability, and the controls we needed before rollout."
AI software engineering for agentic systems
AI agents that move business-critical work.
DeepCurve AI builds controlled agent systems, RAG workflows, coding assistants, and AI automation for teams that need dependable execution, not demos.
Customer Support
Finance Automation
Incident Response
Data Quality
AI Training
Built for AI software that has to perform under operational pressure
Product Engineering
Applied AI Teams
Operations
Finance
Customer Support
Data Platforms
Client testimonials
Signals from serious AI programs.
"DeepCurve AI translated complex retrieval, evaluation, and agent workflow needs into a practical system our users could trust and our team could operate."
"The engagement gave us a sharper path for using AI across research, customer operations, and internal knowledge workflows without compromising review discipline."
"DeepCurve AI brought structure to a complex knowledge workflow. The result was a clearer operating model for retrieval, evidence review, and accountable AI assistance."
"Their work helped frame AI as controlled workflow infrastructure: measurable, auditable, and aligned with the standards expected in financial services."
Support Copilot
A customer support assistant that understands customer context, searches approved
knowledge, drafts replies, remembers useful facts, and pauses for human approval.
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Built for support teams that need faster response drafting without losing control over customer communication. The assistant prepares contextual replies, records useful customer facts, and routes outbound email through an approval step.
- Customer context and approved support knowledge in one workflow.
- Human review before sending sensitive messages.
- Auditable support activity and reusable memory for future conversations.
Account Research Agent
A sales and research workflow that builds structured account briefs, captures
sources, highlights risk signals, and prepares CRM-ready summaries.
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Designed for go-to-market teams that need consistent account intelligence before outreach. The agent coordinates research, source capture, competitor signals, and implementation risk notes into a structured brief.
- Standardized account briefs for sales and customer success teams.
- Source-backed research and audit-friendly summaries.
- Output shaped for CRM ingestion and follow-up planning.
Invoice Reconciliation Agent
A finance operations assistant that compares invoices with purchase orders and
policy rules, explains discrepancies, drafts vendor messages, and gates approvals.
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Built for finance teams that need consistent invoice checks before payments move. The agent explains mismatches in plain language while business rules decide the actual reconciliation status.
- Invoice, purchase order, vendor, and policy review in one flow.
- Clear discrepancy explanations for finance operators.
- Approval gates for payment requests and vendor communication.
Document Ingestion Workflow
A controlled ingestion flow for PDFs and scanned documents that extracts structured
information into reviewable drafts before it is accepted into business records.
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Designed for teams that receive operational data in messy documents. The workflow extracts candidate fields, presents a reviewable draft, and only persists data after validation.
- Structured extraction from text PDFs and scanned documents.
- Review-before-save flow for higher confidence data entry.
- Fallback handling when source quality is low.
Incident Response Agent
An operations assistant that reviews logs and runbooks, investigates likely causes,
estimates blast radius, proposes remediation, and requires approval before action.
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Built for operations teams that need faster incident analysis without giving an agent unchecked production authority. It gathers evidence, explains likely causes, and separates recommendation from remediation.
- Runbook and log review for faster investigation.
- Blast-radius analysis and remediation planning.
- Approval gates before restarts, rollbacks, or resolution actions.
Data Quality Agent
A data operations agent that profiles datasets, runs quality checks, produces
durable reports, opens remediation items, and supports approval-based certification.
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Designed for analytics and platform teams that need repeatable dataset quality reviews. The agent creates durable reports, explains quality issues, and supports approval-based certification before downstream use.
- Dataset profiling and deterministic quality checks.
- Persistent reports for audit and operational follow-up.
- Certification workflow for trusted data assets.
Coding Agent
A repository-aware engineering assistant that explores codebases, plans changes,
edits files, runs checks, protects existing work, and summarizes patches for review.
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Built for engineering teams that want AI help inside real repositories. The agent works through exploration, planning, implementation, testing, and review while preserving developer control over risky actions.
- Codebase exploration and task planning.
- Patch generation, test execution, and concise change summaries.
- Safety boundaries to avoid overwriting active work.
Agentic RAG Assistant
A knowledge assistant for complex questions that plans retrieval, searches multiple
sources, verifies citations, and synthesizes grounded answers.
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Designed for teams that need answers from mixed internal knowledge sources. The assistant decomposes complex questions, searches the right evidence, verifies citations, and avoids unsupported answers.
- Multi-step retrieval planning for complex questions.
- Citation verification for material claims.
- Grounded synthesis with refusal when evidence is insufficient.
Finance QA RAG
A finance question-answering system that retrieves from policy and reporting corpora,
ranks evidence, cites supporting material, and tracks answer quality before rollout.
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Built for finance and compliance teams that need reliable answers from policy, reporting, and operational documents. Retrieval quality is measured before answer generation is trusted.
- Evidence ranking for finance-specific questions.
- Answer generation with citations and confidence metadata.
- Quality tracking before production rollout.
AI Quality Evaluation Suite
A regression and observability workflow that records agent behavior, compares
answers, evaluates retrieval quality, and catches quality drops before release.
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Designed for teams that need confidence before deploying agent changes. The suite captures traces, compares outputs, checks retrieval quality, and turns failures into actionable regression work.
- Behavior traces for debugging and review.
- Regression datasets for agent and RAG workflows.
- Quality gates before updates reach users.
Services
AI engineering for teams shipping agentic software.
Custom AI systems from workflow map to production launch.
We work with you to understand the workflow, data, risk boundaries, and business goal, then deliver a tailored AI solution that your team can operate.
Build & Deploy AI Agent Systems
Production-quality AI agents tailored to support, finance, operations, data, and engineering use cases.
Data Integration & Prep
Connect documents, databases, APIs, and operational systems into reliable AI workflows.
Model Management
Compare, customize, and manage model choices so each workflow has the right quality, cost, and latency profile.
Performance & Quality Assurance
Assess agent behavior, retrieval quality, answer correctness, side-effect safety, and regression risk.
RAG-Based Knowledge Systems
Build scalable knowledge retrieval systems that cite sources and support internal decision-making.
AI Governance
Define approval gates, audit trails, policy controls, and accountability across the AI workflow.
Build real agents. Prove quality. Ship to production.
A production-focused weekend course for building deep agents, production RAG, evaluation workflows, and coding agents. The course is built around real-world agent projects for support, account research, invoice reconciliation, incident response, data quality, coding, and retrieval-heavy knowledge work.
Duration
1 month
4 weeks
Weekend classes
4 hours/week
2 hours Saturday, 2 hours Sunday
Course overview
Students learn agent architecture, durable state, long-term memory, human approval workflows, observability, evaluation, coding-agent workflows, advanced RAG, and deployment readiness.
Additional advanced projects
Coding Agent
A coding assistant with file operations, task planning, patching, tests, sandbox controls, and approval boundaries.
Observability & Evals
Trace agents, debug tool calls, evaluate retrieval and answers, compare prompts, and build regression datasets.
Complex Agentic RAG
A multi-step research assistant that plans retrieval, verifies citations, and synthesizes grounded answers.
Production Hybrid RAG
A finance QA system with search, ranking, retrieval evaluation, and production tuning.
Weekly schedule
Week 1
Foundations & core agents: agent loops, durable state, memory, tools, and support copilot extension.
Week 2
Multi-agent & finance agents: account research, invoice reconciliation, structured outputs, and auditability.
Week 3
Operations & data agents: incident response, diagnostics, data quality reports, and certification workflows.
Week 4
Advanced agents, RAG & production: coding agents, evaluations, agentic RAG, hybrid RAG, capstone, and deployment.
Learning outcomes
- Design agent workflows with trusted runtime context.
- Add memory, policies, approvals, and structured outputs.
- Build coding agents that edit, test, review, and generate patches safely.
- Create RAG systems with retrieval planning, citations, quality checks, and deployment notes.
- Package a production-quality capstone with docs, tests, observability, and a deployment plan.
Production checklist used across projects
- Runtime context is trusted and not model-supplied.
- Every side effect is isolated behind an explicit tool or API.
- High-risk side effects require human approval.
- Deterministic checks own business truth.
- Agent memory is namespaced by tenant or operational subject.
- Read-only files are protected by permissions.
Prerequisites
Comfortable Python programming, REST API basics, SQL familiarity, and basic understanding of LLMs.
Deliverables
Working agent APIs, approval workflows, quality dashboards, production deployment notes, and capstone demo.
Assessment
Weekly labs, mid-course project, RAG evaluation assignment, and a production capstone.
Outcome
A portfolio of practical agent systems for support, operations, finance, data quality, coding, and RAG.
About DeepCurve AI
Built by engineers who treat AI as production software.
Mohammad Shaheer Zaman
Founder
Shaheer is a software engineer with over 15 years of experience in the software industry, including work with Qualcomm, AMD, Wells Fargo, Deloitte, and EY. He is focused on architecting and building AI-based software systems that solve complex operational and engineering problems.
DeepCurve AI works with teams to design transformative AI solutions, integrate them with real business workflows, and make them reliable enough for production use.
View LinkedIn profileContact
Bring the workflow. Leave with the system plan.
Email shaheer@deepcurve.in or send the form below.
Plot No 2, Noor Nagar, Road No 10Banjara Hills, Hyderabad 500034
Telangana, India