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CuriousDevs

Services

AI engineering, from decision to deployment.

We work across the full life of an AI system: deciding where it creates value, engineering it into your products and operations, and operating it once it is live. Ten services, organised in four practices, each delivered by the engineers who build and support it.
Advisory
Strategy and readiness
Applied AI
Agents, LLMs, models, vision
Platforms
Business systems and products
Operations
Private AI and MLOps

Four practices

Choose where to begin.

Most engagements start in one practice and extend into others as a system moves towards production.
Line illustration of a value-against-effort matrix with one highlighted priority

P.01

Advisory

Decide what to build, what to buy and what to leave alone — before money is committed.

Line illustration of an agent coordinating a set of connected tools

P.02

Applied AI Engineering

Language models, agents, retrieval, custom models and vision — engineered as production software.

Line illustration of business records flowing into an AI-assisted workflow

P.03

Software and Data Platforms

The business platforms, products and data foundations that AI runs inside.

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P.04

AI Operations

Hosting, private deployment, monitoring and cost control once a system is live.

P.01 · Practice

Advisory

AI decisions made on evidence rather than enthusiasm. We assess where AI changes the economics of your operations, what it will cost, and what has to be in place for it to work.

01

AI Strategy and Readiness Assessment

An evidence-based view of where AI will create value in your organisation, what it will cost to build and run, and what must be true of your data and teams before it can work.

The problem

  • AI initiatives that start from tools rather than business problems
  • Pilots that succeed in demonstrations but never reach production
  • No reliable view of build, run and model-usage costs before commitment
Discuss an assessment

What we engineer

Discovery
Process mapping with operating teams to locate the decisions, hand-offs and repetitive work where AI changes the economics.
Data and systems
Assessment of data quality, access, integration points and security constraints that determine what is feasible.
Economics
Cost models covering engineering, infrastructure and per-request model usage, compared with the current cost of each process.
Governance
Policies for data handling, human review, model risk and acceptable use, aligned with the Digital Personal Data Protection Act, 2023.

The outcome

A prioritised portfolio of use cases with costed business cases, a sequenced roadmap, and a governance framework your leadership can approve and your teams can operate.

P.02 · Practice

Applied AI Engineering

The core of our work. We engineer AI systems with the same discipline as any software your business depends on: specified behaviour, evaluation before release, access control and full observability.

02

Generative AI Applications

Production applications and copilots built on large language models, integrated into your products and internal tools with the controls that enterprise use requires.

The problem

  • Knowledge workers spending hours reading, drafting and summarising
  • Prototypes that cannot handle real data, permissions or load
  • Model outputs that cannot be traced, tested or explained
Discuss an application

What we engineer

Architecture
Model selection across OpenAI, Anthropic, Google and open-weight models, behind an abstraction layer so the model can change without a rewrite.
Controls
Structured outputs, guardrails, role-based access and human approval for consequential actions.
Evaluation
Task-specific test sets and automated regression checks that run on every prompt or model change.
Integration
APIs, single sign-on and audit logging that fit your existing architecture and pass your security review.

The outcome

A production LLM application with documented behaviour, measured quality and predictable cost — maintainable by your team or operated by ours.
03

Agentic Systems and Workflow Automation

AI agents that plan and carry out multi-step work across your systems — updating records, raising documents, scheduling and escalating — within permissions you define.

The problem

  • Operational work that consists of moving information between systems
  • Rule-based automation that breaks on unstructured inputs and exceptions
  • Justified concern about software acting without oversight
Discuss a workflow

What we engineer

Orchestration
Single- and multi-agent architectures using LangGraph, the Model Context Protocol and durable workflow engines such as Temporal.
Tool integration
Typed connectors to CRM, ERP, email, calendars, databases and internal APIs.
Oversight
Scoped permissions, approval checkpoints, rate limits and complete action logs that can be audited and reversed.
Automation
Deterministic workflows in n8n or code wherever an agent is not the right tool for the job.

The outcome

Processes that complete end to end with measured throughput and error rates, and a complete record of every action taken on your behalf.
04

Conversational and Voice AI

Voice and messaging agents for customer-facing operations — reception, lead qualification, appointment scheduling, payment follow-up and support — in English, Hindi and other Indian languages.

The problem

  • Missed calls and slow responses to high-intent enquiries
  • Contact-centre capacity that cannot match peaks in demand
  • Conversations that leave no structured record in the CRM
Discuss a voice agent

What we engineer

Telephony
Inbound and outbound calling over SIP and Indian telephony providers, with recording, transfer and IVR modernisation.
Messaging
Agents on the official WhatsApp Business Platform, web chat and Instagram, using approved message templates.
Speech
Streaming recognition and synthesis with natural turn-taking and interruption handling, tuned for Indian accents.
Agent library
Receptionist, lead qualification, appointment, payment follow-up, support and internal knowledge agents, configured to your scripts and systems.

The outcome

Every enquiry answered and recorded, a person brought in when it matters, and conversation data flowing directly into your CRM.
05

Enterprise Search and Knowledge Systems

Retrieval-augmented systems that answer questions from your documents and data with citations, enforcing the access rights your organisation already defines.

The problem

  • Institutional knowledge scattered across drives, wikis, tickets and databases
  • Search that returns documents when people need answers
  • Risk of AI exposing information to people who should not see it
Discuss a knowledge system

What we engineer

Ingestion
Connectors for SharePoint, Google Drive, Confluence, Notion, ticketing systems and relational databases, with incremental synchronisation.
Retrieval
Hybrid lexical and vector search with re-ranking, tuned on your own queries.
Access control
Document-level permissions enforced at retrieval time, before anything reaches the model.
Quality
Answer accuracy and citation coverage measured on a curated set of real questions from your teams.

The outcome

Accurate, sourced answers for employees and customers, with measured accuracy and no leakage across permission boundaries.
06

Custom Models and Fine-Tuning

Domain-specific models trained on your data, for tasks where general-purpose models are too costly, too slow or too inconsistent.

The problem

  • High-volume tasks where large-model costs do not scale
  • Inconsistent format or tone from prompted general models
  • Data that cannot be sent to third-party model providers
Discuss a model

What we engineer

Data
Dataset design, cleaning, labelling and synthetic data generation, with quality controls at each step.
Training
Parameter-efficient (LoRA, QLoRA) and full fine-tuning of open-weight models, and distillation from larger models.
Evaluation
Comparison against the base model and prompted alternatives on held-out test sets before any deployment decision.
Serving
Optimised inference with vLLM on GPU infrastructure, hosted in India or in your own environment.

The outcome

A model you own outright, with documented evaluation results, lower cost per request and behaviour that meets your specification.
07

Document Intelligence and Computer Vision

Extraction, classification and inspection systems that turn documents and images into validated, structured data.

The problem

  • Manual data entry from invoices, forms, KYC and trade documents
  • Errors discovered only after data has entered core systems
  • Visual inspection that depends on individual attention
Send sample documents

What we engineer

Document AI
Layout-aware extraction from scanned and digital documents, including tables, handwriting and engineering drawings.
Validation
Cross-checks against ERP records, master data and business rules before anything is posted.
Vision
Detection and classification models for quality inspection, safety monitoring and inventory.
Review
Confidence-based routing so people review only uncertain fields, with a full audit trail.

The outcome

Straight-through processing for routine documents, exceptions surfaced for review, and every extracted field traceable to its source.

P.03 · Practice

Software and Data Platforms

AI only creates value inside the systems people use every day. We build those systems — business platforms, products and data pipelines — with AI designed in from the start.

08

AI-Native Business Platforms

CRM, ERP and HRMS platforms designed with AI inside the workflow — or AI capabilities added to the platforms you already operate.

The problem

  • Business software that records work instead of doing it
  • Teams operating across spreadsheets and disconnected tools
  • Off-the-shelf platforms that do not fit Indian business processes
Discuss a platform

What we engineer

CRM
Lead capture from every channel, scoring, automated follow-up and conversation summaries inside the pipeline.
ERP
Inventory, procurement and finance modules with AI-assisted reconciliation, anomaly detection and forecasting.
HRMS
Employee records, attendance, leave and payroll, with assistants for policy queries and candidate screening.
Extension
AI capabilities integrated into Zoho, Odoo, Salesforce, Tally and custom systems through their APIs.

The outcome

A platform your teams adopt because it removes work, with data structured for reporting and further automation.
09

Product and Platform Engineering

Full-stack engineering for web applications, backend platforms, real-time systems and data pipelines — the foundations that AI capabilities depend on.

The problem

  • Products that need AI features but lack engineering capacity
  • Backends that cannot sustain real-time load or event volumes
  • Data spread across systems with no reliable pipeline
Discuss a product

What we engineer

Product
Web applications in Next.js and TypeScript, built for accessibility and performance.
Backend
Services in Go, Python and Node.js with well-defined APIs, authentication and observability.
Real-time
Event-driven architectures on Kafka and WebSockets for voice, messaging and other real-time workloads.
Data
Pipelines, warehousing and analytics on PostgreSQL and modern data tooling.

The outcome

Production software with tested code, documentation and deployment pipelines — delivered by engineers embedded in your team or against a fixed scope.

P.04 · Practice

AI Operations

Launch is the beginning of an AI system, not the end. We operate what we build — or what others built — with monitoring, cost control and an accountable team.

10

AI Infrastructure, MLOps and Managed Operations

Deployment and operation of AI systems hosted in India or inside your own environment, with monitoring, cost control and on-call support.

The problem

  • AI systems in production that no one is accountable for
  • Data-residency or regulatory requirements that rule out public APIs
  • Model and usage costs that grow without visibility
Discuss operations

What we engineer

Deployment
Private deployment of open-weight models and applications on Kubernetes — in your cloud account, on-premises, or hosted in India.
Observability
Tracing, quality monitoring, drift detection and alerting with OpenTelemetry, Prometheus and Grafana.
Cost control
Attribution of model usage to teams, features and customers, with routing and caching to reduce spend.
Operations
Incident response, scheduled evaluation runs and monthly improvement cycles under an agreed service level.

The outcome

AI systems that stay reliable, secure and cost-predictable after launch, with a named engineering team accountable for them.

Start with the problem

Describe what is slow, costly or error-prone today. We will tell you which of these services applies — or that none does.

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