AI & Automation ServicesAI that does the work, not a demo of the work.
We build custom agents, document intelligence, and workflow automation around how your operation actually runs — with evaluation, guardrails, and a defined answer for what happens when the model is wrong.
Why do most AI pilots never reach production?
Most organisations have already tried this once. The demo worked, everyone was impressed, and eighteen months later the process is still being done by hand.
- 01It worked on the examples someone chose
- Curated inputs hide the real distribution — the scanned page, the exception, the form filled in wrong. Without an evaluation set built from actual data, nobody finds out where it breaks until it is in front of a customer.
- 02Nobody answered what happens when it is wrong
- No confidence threshold, no review queue, no audit log, no named owner. So the work sits in limbo: too promising to cancel, too unaccountable for anyone to approve for real use.
- 03The automation is one person's pile of scripts
- It runs on someone's laptop or an unmonitored account, breaks silently when an API changes, and nobody else can safely touch it. That is not automation, it is a dependency with a single point of failure.
All three are the same gap: AI treated as a feature demo rather than as production software with failure modes.
Built to be trusted with real work.
We build an evaluation set from your real data and measure against it before anything goes live. You see the accuracy number, and the cases it fails on, while it is still a decision rather than an incident.
Confidence thresholds, review queues, escalation paths, and audit logs are part of the solution design. Oversight added after an incident is a patch; designed in, it is what makes approval possible at all.
Discovery maps the process and its real volumes first. If the honest answer is a form, a rule, or a fixed integration rather than a model, we will tell you — a cheaper solution that works is still the better outcome.
Automation lives or dies at the integration boundary. The same senior team that does the backend and architecture work owns that boundary here, so an agent is wired into your systems properly rather than bolted to their edges.
What AI and automation services does Devarithm offer?
A first engagement usually takes one process end to end rather than all of these at once. Discovery decides which process, and whether it is worth automating at all.
Automation Discovery & Process Mapping
Before anything is built: what the process actually does, how often, how long it takes, and where the exceptions live. This is also where some candidates get ruled out, which is the cheapest possible outcome.
- Process map with volumes and time cost
- Ranked automation opportunities
- A written build-or-do-not-build recommendation
- Success metrics agreed before development
Custom AI Agent Development
Agents that do a defined job inside your systems — reading, deciding, and acting through real tools, with explicit limits on what they are allowed to do unsupervised.
- Agent architecture and tool design
- Prompt and context engineering against real cases
- Integration with your existing APIs and tools
- Fallback, escalation, and refusal behaviour
Document Intelligence
Extraction from the documents your operation runs on — invoices, claims, contracts, applications — including the ones that are scanned, rotated, or filled in by hand.
- Extraction pipeline for your document types
- Field validation and confidence scoring
- Human review queue for low-confidence output
- Structured output written into your systems
RAG & Knowledge Pipelines
Answers grounded in your own documentation rather than in the model's memory, with a citation attached so a person can verify the source in one click.
- Ingestion and chunking strategy for your content
- Vector store setup and retrieval tuning
- Source citation in every answer
- Refresh strategy for content that changes
Workflow Automation & Integration
The wiring around the intelligence: triggers, scheduling, retries, and handoffs between systems and people. Most of the durable value of an automation lives in this layer.
- Integration with your existing tools and systems
- Trigger, scheduling, and queueing logic
- Error handling, retries, and dead-letter paths
- Notifications and human handoff points
Evaluation, Guardrails & Monitoring
The layer that makes the rest approvable: a measured quality baseline, limits on what the system can do or say, and visibility into what it did while nobody was watching.
- Evaluation set and measured quality baseline
- Guardrails for unsafe or out-of-scope output
- Token cost monitoring and rate limiting
- Audit logs, usage dashboards, and alerting
Not sure which process is worth automating?
Tell us where the week actually goes. We will tell you what is worth automating, what is not, and what each one would take.
No commitment required. No pitch deck. Just a focused 30-minute conversation.
How does Devarithm build AI automation?
Five stages. The evaluation set is written before the build starts, because "it seems to work" is not a launch criterion.
- 01Week 1
Discovery & Process Mapping
We sit with the people who do the work and map it as it actually happens, including the exceptions they handle without thinking about them. Opportunities come out ranked by value and difficulty.
You getProcess map, ranked opportunities, agreed success metrics
- 02Week 1–2
Design & Evaluation Plan
Solution design — models, tools, integration points, and the human oversight path — alongside the evaluation set the build will be judged against. Both are agreed before anyone writes code.
You getSolution design and the evaluation set
- 03Week 2–5
Build & Integrate
The automation is built against your real data in your environment, wired into the systems it needs, with logging from the first commit rather than added when something goes wrong.
You getWorking automation running in your environment
- 04Week 5–6
Evaluate & Harden
We measure against the evaluation set, tune what falls short, and put the guardrails and review queue in place. You approve going live on a number, not on a demo.
You getMeasured accuracy, guardrails, and a human review path
- 05Week 6+
Deploy & Monitor
Production deployment with monitoring, cost controls, and alerting, plus a runbook your team can operate from. Accuracy and cost keep getting tuned against real usage after launch.
You getLive automation with monitoring, cost controls, and a runbook
What tech stack do we use for AI automation?
Defaults, not dogma. Model and tool choice follow the task and its cost profile, and we build on what your team already runs wherever it is sound.
- Models
- Claude APIOpenAI APIGemini APIOpen-weight models
- Orchestration
- LangChainLlamaIndexn8nTemporal
- Retrieval & Data
- pgvectorPostgreSQLPineconeRedis
- Integration
- Custom APIsWebhooksZapierMake
- Infrastructure
- AWSDockerGitHub ActionsVercel
Most workflows route by task: a frontier model for the genuinely hard cases, a smaller and cheaper one for the bulk. Running everything through the largest available model is the usual reason a pilot never survives its own unit economics.
Grounding answers in your documents is cheaper, updates the moment the document does, and can cite its source. Fine-tuning is worth it for format and tone, rarely for knowledge.
What the system read, what it decided, what it changed, and how confident it was. An automation nobody can audit is one nobody will be allowed to trust with real work.
Who is this service built for?
This is also a filter. If none of these describe you, the discovery call will be short and free, and we will tell you honestly.
- Operations Teams Doing Manual Work
- A meaningful share of the week goes to copying between systems, triaging inboxes, and re-keying data that already exists somewhere else.
- One process automated end to end, measured against the time it actually cost
- Companies With a Document Bottleneck
- Invoices, claims, contracts, or applications arrive faster than people can read them, and the backlog is now a customer-facing problem.
- Extraction with confidence scoring and a review queue for the hard cases
- Teams That Ran a Failed AI Pilot
- The demo impressed everyone and never reached production, because nobody could answer what happens when it is wrong.
- Evaluation, guardrails, and an audit trail — the reasons it stalled, addressed directly
- Product Teams Adding AI Features
- AI features are on the roadmap for a product you already run, and the open questions are quality, cost, and what users see when the model fails.
- Feature design with evaluation, fallback behaviour, and cost controls
Start with one process, not a programme.
A single workflow taken end to end proves the case far better than a strategy deck, and it is the cheapest way to find out whether this works for you.
Thirty minutes to scope it, no obligation.
How can you engage Devarithm for automation work?
Indicative ranges for AI and automation work. We give you a scoped figure after discovery, not before it — but you should not have to book a call to find out the order of magnitude.
A defined automation with a clear end state — one agent, one document pipeline, or one workflow taken end to end.
- Fixed scope and agreed success criteria
- Evaluation harness and guardrails included
- Integration with your existing systems
- Best for: a first automation done properly
Continuous automation delivery for teams rolling this out across more than one process.
- Monthly delivery against a priority queue you control
- Monitoring, evaluation, and cost tuning included
- Model and prompt maintenance as APIs change
- Best for: rolling automation out across a team
A fixed two-to-four week window on a single workflow, with no commitment past it.
- One process, end to end, fixed window
- Includes the honest verdict on whether to continue
- No long-term commitment
- Best for: proving the case before funding a programme
What actually moves your number: how clean the input data is, how many systems the workflow touches, the accuracy bar the process requires, and whether a human approval step is required by regulation.
Start with one process.
Tell us which part of the week your team loses to manual work. We will tell you whether it is worth automating, what it takes, and what it costs — including when the answer is not to build it.
No commitment. No 50-slide deck. A focused 30-minute call.
Frequently Asked Questions
What is AI automation?
AI automation is the use of language models and related tooling to carry out steps of a business process that previously needed a person — reading documents, classifying requests, drafting responses, or deciding what happens next. It differs from traditional automation in that it handles unstructured input and ambiguity, which is also why it needs evaluation and human oversight that rule-based automation does not.
Which processes are worth automating?
High-volume, repetitive work with unstructured input and a tolerable error cost — document extraction, request triage, data entry between systems, first-draft generation, and internal knowledge lookup. Low-volume or high-stakes irreversible decisions usually are not worth it, and discovery is where we say so.
What happens when the AI gets something wrong?
That is decided in the design, not discovered in production. Every automation gets a confidence threshold, a defined fallback, a human review queue for uncertain cases, and an audit log. The honest position is that models are wrong sometimes, so the system is built to fail into a person rather than into a customer.
How accurate will it be?
We measure it against an evaluation set built from your real data and report the number before launch rather than promising one beforehand. Accuracy depends heavily on input quality and how narrow the task is, and a quoted figure from another project tells you nothing about yours.
How much does AI automation cost?
Project-based automations typically run ₹4L–₹15L (roughly $5,000–$18,000) for one process taken end to end. Ongoing model usage is billed to your own provider accounts, and cost monitoring is part of what we build so it never surprises you.
Which AI models do you use?
Claude, OpenAI, Gemini, and open-weight models, chosen per task rather than by preference. Most production workflows route by difficulty — a frontier model for the hard cases and a smaller one for the bulk — which is usually the difference between an automation that pays for itself and one that does not.
Will our data be used to train AI models?
No. We build on API tiers that do not train on customer data, keep your data in your own infrastructure and provider accounts wherever the design allows, and agree data handling in writing before the build starts.
How long does an AI automation project take?
Six to eight weeks for a single process from discovery to production, including the evaluation and hardening stages. A focused sprint on one workflow can run in two to four weeks when the integration surface is small.
Can you work with the tools we already use?
Yes. Automation is mostly integration work, and we build into your existing systems through their APIs rather than asking you to migrate. Where a system has no API, we will tell you what the realistic options are before you commit.
Do you support the automation after it is deployed?
Yes. Lifetime support is the default engagement here rather than an upsell. It matters more in this service than most: model APIs change, prompts drift, and an automation nobody maintains degrades quietly.
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