AI Development

Applied AI Consulting and Services for Real Business Outcomes

Predictive analytics, workflow automation, and LLM integration applied to the cost and revenue levers that matter.

Overview

AI applied to cost and revenue levers, not science experiments

Our applied AI consulting practice builds AI into real business processes. Predictive analytics, workflow automation, and LLM integration - scoped to a measurable outcome, validated against ground truth, and rolled out with monitoring and human-in-the-loop. As an applied ai company delivering applied ai services, we ship AI, not demos.

Years in business: 12
Team members: 65+
Global clients: 30+
Yr avg. client retention: 4+

Who this is for

Operations and supply-chain leaders who need demand, inventory, or staffing forecasts that hold up under volatility.
Marketing and finance teams who need churn, LTV, and cohort modeling tied to live decisions.
Customer service and operations teams looking to automate repetitive workflows with LLMs.
Product teams adding AI features that have to behave consistently in production.

What you get

Applied AI roadmap - a sequenced list of business outcomes AI can move, with the simplest method that hits each target.
Predictive analytics builds - demand, churn, LTV, propensity, anomaly detection, modeled, validated, and integrated.
Workflow automation - LLM-assisted automation for ticket triage, document processing, content classification, and approval routing.
LLM integration - APIs, prompts, function calling, retrieval grounding, evaluation, monitoring.
Production hardening - model registry, deployment, monitoring, drift detection, retraining schedules.
Human-in-the-loop design - where AI decisions touch revenue, customers, or compliance, we design the human checkpoint upfront.

How we work

01 Step

Audit

Identify the cost or revenue lever AI will move. Map data, success metrics, and the cost of being wrong.
02 Step

Plan

Pick the simplest method - heuristic, classical ML, or LLM - that hits the target. Define guardrails and human checkpoints.
03 Step

Build

Ship the model and the integration in increments. Each increment is validated against ground truth.
04 Step

Test

Offline eval (accuracy, precision, recall, F1, RMSE) and online eval (A/B against existing decision) before scale.
05 Step

Scale

Layer monitoring, drift detection, retraining, and human-in-the-loop where the cost of wrong matters.

Tools & stacks we use

The platforms our team is fluent in for this practice. Most engagements span a few of these, picked for the actual problem rather than for the demo.

Python
scikit-learn
statsmodels
XGBoost
LightGBM
Prophet
Statsforecast
Darts
Snowpark ML
PyTorch
TensorFlow
OpenAI
Anthropic
LangChain
MLflow
Weights & Biases
SageMaker
Databricks Model Serving
Airflow
Prefect
Temporal
Evidently
Arize

Need dedicated experts?

Hire a specialist embedded with your team

Pre-vetted senior talent for this practice - hourly, retainer, dedicated FTE, or Micro-GCC. Vetted in 48 hours, managed end-to-end by H4H operations.

AI / ML Engineer Hire a AI / ML Engineer Python Developer Hire a Python Developer

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Frequently asked questions

Still have a question? Talk to a real human on our team - we usually reply within one business day.

Talk to a specialist

What is applied AI and how is it different from research AI?

Applied AI is AI built to ship - measured by business outcome, not paper acceptance. The model can be simpler than the state of the art if it hits the target reliably.

How does H4H run an applied AI engagement?

Audit, Plan, Build, Test, Scale. We start with the cost or revenue lever AI is going to move and pick the simplest method that hits the target.

How much do applied AI services cost?

Project bands depend on whether we are building a predictive model, an automation, or an LLM-integrated workflow. Typical projects start in the mid-five figures. Retainer and dedicated FTE quoted separately.

How long does an applied AI project take?

A scoped predictive model runs 6-10 weeks. A workflow automation runs 4-10 weeks. A full LLM integration into an existing application runs 8-16 weeks.

What results can I expect?

Outcomes we have measured include ~90% forecast accuracy at Bouqs, 10% NPS lift attributable to forecasting, and AI-enabled production platforms (LegalWiz.in) serving 10,000+ clients.

How do you handle data readiness?

We start with a data audit. If the data is not ready, we propose either a data engineering work stream first or a narrower problem we can solve with the data on hand.

Predictive analytics vs LLM-based automation - which one fits my problem?

Predictive analytics wins when the decision is structured, repeated, and the cost of being wrong is measurable. LLM-based automation wins when the input is unstructured (text, documents, conversations) and the work is varied.

How is H4H different from a freelance data scientist?

A freelancer ships a model. We ship the model, the data pipeline that feeds it, the application that uses it, the monitoring that watches it, and the team to keep it running.

Proof points

Related case studies

What we have shipped for clients with adjacent problems. Each one is sourced and attributable.

Bouqs - D2C / Subscription Flowers

~90% forecast accuracy in peak season. NPS +10%.

ML-driven demand forecasting across SKUs cut buffer-stock costs for Mother's Day and Valentine's Day fulfillment.

Read case study

LegalWiz.in - Professional Services / LegalTech

Production platform serving 10,000+ clients across 100+ services

Full-stack platform for a leading professional services company. Process and service builder, CRM, billing engine, AI-powered chat and knowledge library, dynamic questionnaire with upsell, role-driven access.

Read case study

Related services

Ready to put your data to work?

Book a free audit and we will map the problem, the metrics, and the smallest first build that proves value.

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