Staff questions answered from your documents
HR, compliance or product questions answered from approved handbooks and wikis, with a citation a reviewer can check.
AI development that improves real work.
AiRedHQ builds AI assistants, retrieval with citations (RAG), workflow automation and LLM integrations for startups, SaaS products and teams buried in documents. A typical project starts with a 2-week audit and ships a tested pilot in 4 to 6 weeks, with an evaluation set, human review and the cost per request measured.
hiARed
Job posts
Quality checks
Pipeline
Reports
Job Post Quality
Senior Product Engineer
01
Read post
02
Check factors
03
Explain score
04
Recruiter approves
78/100
Salary range and role scope are clear. The post does not say how many interview rounds there are or when candidates hear back; adding both would raise the process score.
Job descriptionHiring process
Salary clarityClear
Role clarityClear
Process clarityMissing
Human reviewRequired
Where AI earns its place.
Move from an AI idea to a dependable product capability grounded in your workflow, data and users.
Document intake and extraction
Resumes, invoices, forms or contracts in PDF and DOCX read into structured fields. Low-confidence items go to a person.
Scores that explain themselves
Rate job posts, leads or applications against a rubric and show which factors raised or lowered the number.
Support triage and reply drafts
Classify incoming tickets or emails, draft a reply, and let an agent approve or edit it before it is sent.
Who our AI development services are for.
Teams that already have a workflow worth improving, and the documents or data to ground it.
- Operations teams buried in documents, forms or email that follow a repeatable pattern.
- SaaS products that want an AI feature users trust, not a demo bolted onto the side.
- HR, support and compliance teams that need answers drawn from approved internal knowledge.
- Founders validating an AI product idea who need a pilot on real data before a full build.
- Companies that tried a chatbot, saw it make things up, and want a grounded version.
AI with review built in.
Our work on hiARed informs how we design explainable AI workflows, human review and multi-role decision systems.
AI Assistants
Task-focused assistants designed around real roles, permissions and business context.
RAG Systems
Retrieval pipelines that give models relevant, governed and traceable context, with citations back to the source.
Workflow Automation
Human-aware automation for repetitive operational and knowledge workflows.
LLM Integration
Model capabilities embedded into products with evaluation, safeguards and observability.
What you get.
Concrete artifacts you keep, whoever builds the next version.
Use-case brief
The workflow, users, success measure and the cost of a wrong answer, agreed before anything is built.
Evaluation set with acceptance thresholds
Real examples with expected answers, and the pass rate the system has to reach before it ships.
Retrieval index and prompt registry
Versioned prompts and indexed sources, so every change can be traced, compared and rolled back.
Human-review screen
Where people approve, correct or reject output. Corrections flow back into the evaluation set.
Cost-per-request estimate
Model, token and infrastructure cost at your expected volume, with a cheaper fallback where one exists.
Monitoring dashboard
Quality, latency, cost and human-override rate tracked after launch, with alerts when they drift.
From use case to monitored model.
- 01
Use case
Map the workflow, the users and what a good answer looks like.
Output: Use-case brief
- 02
Data
Audit sources, access rules and sensitive fields.
Output: Data map and access plan
- 03
Prototype
Build a working slice against your real examples.
Output: Working prototype
- 04
Evaluate
Score outputs against the evaluation set and compare models.
Output: Evaluation report
- 05
Integrate
Connect to your systems with review steps, permissions and logging.
Output: Production release
- 06
Monitor
Track quality, cost and overrides, then tune prompts and retrieval.
Output: Monitoring dashboard
RAG, fine-tuning or plain automation?
Most AI integration work falls into one of three patterns. Choosing the right one early saves months and keeps running costs predictable.
RAG chatbot or search
Choose retrieval-augmented generation when answers must come from your own documents and change as they change. Citations make every answer checkable, and new knowledge means re-indexing, not retraining.
Fine-tuning
Choose fine-tuning when you need a consistent format, tone or classification at high volume and have hundreds of good labelled examples. It does not reliably add new facts, so it is often paired with RAG.
Rules with one AI step
Choose plain workflow automation when the steps are predictable. Many AI automation requests are cheaper and more reliable as rules, with a model used only at the messy step, such as reading a document.
Ways to work together.
Start small and keep going only if it is working. Durations are typical; the first conversation sets the real scope.
Typically 2 weeks
AI opportunity audit
Review workflows and data, shortlist use cases, and estimate value, risk and running cost for each.
Typically 4–6 weeks
Pilot
One use case built on your data, with an evaluation set and review screen, used by a small group.
Typically 8–16 weeks
Production build
Integration, permissions, monitoring and rollout to the wider team.
Monthly retainer
Ongoing improvement
Evaluation runs, model upgrades, prompt and retrieval tuning, and new use cases.
What shapes cost and timeline.
We do not publish fixed prices because scope varies widely. These factors move cost and timeline most; an audit or discovery engagement turns them into a written estimate.
- Number of use cases
- Each workflow needs its own evaluation set and review path.
- Data readiness
- Clean, accessible documents cost far less than scanned PDFs spread across drives.
- Accuracy bar
- A higher pass threshold means more evaluation work and more human review.
- Integrations
- Connecting to your CRM, HR, ERP or ticketing tools with permissions adds build time.
- Request volume
- Model and token cost grows with usage, so volume shapes the model choice.
Common mistakes we help you avoid.
Starting with the model
Picking a model before agreeing what a good answer looks like leaves nothing to test against.
No evaluation set
Without real examples and a pass rate, every prompt change is a guess.
Removing people too early
Full automation before the override rate is low turns small errors into customer-facing ones.
Ignoring running cost
A pilot that is cheap at a hundred requests a day can be expensive at a hundred thousand.
From our own products.
We build and run software of our own. These are the parts that relate to this service.
hiARed
Every job post carries an explainable 0–100 Job Post Quality score, and every company a Company Transparency Report. Each score shows the factors behind it, such as salary, process and role clarity, instead of a bare number.
hiARed
Resume parsing turns PDF and DOCX uploads into structured candidate profiles that recruiters review in the pipeline, with scorecards kept next to the evidence.
Models and data, chosen per task.
Model choice follows the workflow, data sensitivity and the cost of a wrong answer. We usually compare two or three models on your own examples before choosing.
OpenAI
Gemini
LangChain
Node.js
PostgreSQL
RedisRelated products and reading.
Questions about AI projects.
Updated
Where should an AI project begin?
With a clear workflow, user need and success condition. Model selection follows those decisions. The two-week audit exists to get those answers in writing.
Can you work with private company data?
Yes. Architecture is shaped around access controls, data boundaries, retention and the sensitivity of the use case. Sensitive fields can be redacted before they reach a model, and providers are configured so your data is not used for training where they offer that option.
How do you reduce unreliable AI output?
We combine grounded context, structured outputs, an evaluation set with pass thresholds, human review and monitoring, weighted by the risk of the workflow.
Which model will you use?
We test candidates such as OpenAI and Gemini models against your evaluation set and choose on accuracy, latency and cost. The integration stays model-agnostic so you can switch later.
What will it cost to run each month?
You get a cost-per-request estimate during the pilot, based on real prompts and your expected volume. In production we set usage limits and alerts so spend cannot drift unnoticed.
Is AiRedHQ an AI development company in India?
Yes. AiRedHQ is based in India and builds AI into its own products, such as the explainable scores in hiARed, as well as for clients. You get working software to review every two weeks.
Do we need a lot of data to start?
Usually not. A RAG assistant needs the documents it should answer from, and an evaluation set can start with 50 to 100 real questions and expected answers. Fine-tuning is the option that needs large labelled datasets.