AI & automation

AI in the workflow,not in a demo.

We design and build AI and automation solutions that fit into your real-world operations, with human review, strong governance and measurable business impact.

  • RAG
  • Document AI
  • Computer vision
  • Predictive ML
  • Workflow automation
Talk to our teamExplore our approach
AI
  • Document AI
  • Computer vision
  • Predictive ML
  • Governance
  • Human review
  • Search
  • ISO/IEC27001
  • ISO/IEC42001
  • DPA andBAA ready
  • AWS, Azure,Google Cloud
  • All cloudregions

OrbitNexa builds retrieval-augmented LLM applications, document processing, computer vision and predictive models into the systems that run healthcare, banking and operations businesses, in the client's own AWS, Azure or Google Cloud account. Every system ships with an eval harness, a human review queue and a full audit log, under ISO/IEC 27001 and ISO/IEC 42001 certified controls.

What we would automate

By industry, with the review gate each one needs.

Forty-five automations across eight industries in the brief; the six panels below carry the ones we build most.

  • Banking & NBFC

    Extraction, assessment and drafting inside RBI conduct rules.

    KYC extraction and match · income assessment from salary slips · collections drafting · AML alert narratives · fraud anomaly triage

    RBI FREE-AI · DPDP · EU AI Act high-risk (credit scoring)

    ClaudePythonpgvectorAWS

  • Insurance

    Intake, triage and verification with a reviewer queue.

    First notice of loss from email and PDF · claims triage and routing · policy-issuance verification · underwriting from the KYC pack · fraud scoring

    IRDAI · FCA

    GPTDocument AIPostgreSQLAzure

  • Healthcare, RCM & diagnostics

    Documents read, coded and routed with clinician or coder sign-off.

    Eligibility verification · prior-authorisation packets · coding suggestions · lab-report drafting from LIS data · denial-prevention analytics

    HIPAA · DPDP · ABDM · NHS DTAC · NABL

    ClaudeFHIRPythonGoogle Cloud

  • Manufacturing

    Vision on the line and extraction from drawings, at the edge where needed.

    Visual defect detection · CAD and drawing extraction for quoting · predictive maintenance from sensor data · quality-record digitisation

    Operator confirmation on a reject · MES and QMS write-back

    PyTorchYOLOTritonAWS

  • Logistics

    Documents and exceptions handled at the pace of the dock.

    Proof-of-delivery extraction · route and dispatch optimisation · customs-document checks · exception handling

    Idempotent write-back · audit log per shipment

    PythonFastAPIKafkaAzure

  • Legal, finance & HR back office

    Clause extraction, matching and grounded assistants, with a human decision where the law requires one.

    Contract clause extraction and comparison · lease abstraction · invoice-to-PO matching · expense audit · candidate screening with human decision

    EU AI Act high-risk (hiring) · permission-aware retrieval

    ClaudepgvectorNode.jsAWS

AI & Automation Solutions

Document to decision.

  • Measured
  • Reviewed
  • Audited
  • Review queueField, region, score.

Our process

From a measured baseline to a system in production.

An accuracy threshold written down before anything is built, a pilot measured against it, and the same eval set run on production traffic after go-live.

  1. 01

    Discover

    Discovery & Strategy

    1-2 weeks

    We map your ecosystem, constraints and KPIs before any engineering starts, so every technical decision has a reason on record.

  2. 02

    Architect

    Architecture & Design

    2-3 weeks

    Architects draw the system and designers prototype the interface, both reviewed and signed off before a line of code is written.

  3. 03

    Build

    Agile Development

    4-12 weeks

    Iterative sprints on modern frameworks, with a senior engineer reviewing every pull request before it merges.

  4. 04

    Evaluate

    Quality Assurance

    2-4 weeks

    Automated unit, integration and acceptance tests, plus performance and security checks against real-world scenarios.

  5. 05

    Operate

    Launch & Evolution

    Ongoing

    A zero-downtime deployment, then continuous monitoring and iterative enhancement as your standing technical partner.

Production-ready means.

Evals in CI, tracing on every request, a rollback, and a person where the decision needs one.

  1. Evals in CIA golden set agreed in discovery runs on every change, in Ragas, promptfoo or DeepEval. A drop blocks the release.
  2. TracingEvery request traced end to end in Langfuse or LangSmith; the full output log exportable to your SIEM.
  3. MonitoringDrift and data-quality alerts to a named owner, per field or per class.
  4. RollbackPrompt and model versions pinned; a rollback is a revert to the last approved version.
  5. ReviewHuman review checkpoints with reviewer identity logged; prompt-injection and PII controls on every input.

Who is on the engagement.

Overlap with UK and Indian working hours every day, a shared channel, a weekly call and a demo against the eval set every two weeks.

  • Engagement leadA senior AI engineer, not an account manager
  • Two to three AI engineersRAG, document AI, vision or ML as the brief needs
  • Integration engineerERP, LIS, CRM or core system connectors
  • Data engineerWhen the sources need a modelled layer first
  • Your reviewersOwn the golden set and the review queue
The full process, phase by phase

Let’s build together

Have an idea?
Let’s make it real.

Tell us about your goals. We’ll help you find the right way forward.

  • Share your idea

    Tell us what you're looking to build.

  • Explore possibilities

    We'll understand your goals and suggest the right approach.

  • Plan the next steps

    Together we define the roadmap.

  • Build what's next

    Turn ideas into real impact.

Let’s discuss
your project

Whether it’s a new product, a platform upgrade or a complex challenge, we’re here to help.

Get in touch

Engagement models

Start with one workflow. Everything after it is optional.

A pilot moves to production when the measured accuracy clears the agreed threshold and the review design is signed.

  • Targeted Automation Pilot

    One workflow, one system, automated end to end on your data and measured against a threshold agreed on day one.

    TeamEngagement lead, two AI engineers, your reviewers
    Timeline4 – 6 weeks
    Key deliverablesA working pilot, a measured accuracy figure and a go/no-go in writing

  • Production AI Build

    A RAG, vision or ML system taken to production: integrations, review gates, monitoring and the audit log.

    TeamLead, AI and integration engineers, data engineer as needed
    Timeline8 – 16 weeks
    Key deliverablesA system in production your compliance team can trace end to end

  • AI Operations Retainer

    Monitoring, retraining, evals and a governance review every month.

    TeamA named engineer, on the system
    TimelineOngoing
    Key deliverablesA monthly record of how the system behaved, and connectors kept current

Start with an automation opportunity assessment

2 – 4 weeks

Process walk-throughs, a data and system access review, and a scored shortlist of automations with an accuracy threshold and a review design for each. You leave with the written assessment, one architecture sketch per shortlisted process, and a go/no-go on a pilot.

Book the assessment

Where your data goes.

Your account, your region, a published sub-processor list, and a no-train clause on every model.

  • Your cloud account

    AWS, Azure or Google Cloud, India region by default. Open-weights models in-account when data cannot leave it.

  • You choose which models see what

    The routing table is a deliverable: public data to a hosted model, personal, clinical or financial data to an in-account or DPA/BAA-covered endpoint.

  • DPA and BAA available

    Covering us and every sub-processor in the chain, with a no-train clause on every endpoint.

  • You own everything at the end

    Code, prompts, eval sets, models, infrastructure code and logs, in your repositories and accounts from day one.

Regulated by design

What we deliver under the EU AI Act, RBI FREE-AI, DPDP, HIPAA and NHS DTAC.

Compliance posture you can read in the first two screens: where data goes, who reviews what, and how accuracy is measured.

Regulated-industry readiness

  • EU AI Act deployer duties (Art. 26) — oversight design, six-month log retention, worker and customer notices, incident process, the technical-file section for high-risk uses
  • RBI FREE-AI and model-risk guidance — model inventory entry, independent validation report, customer disclosure text, challenge channel hook, vendor accountability statement
  • DPDP Act and Rules 2025 — processor contract clauses, purpose-limited training, India region pinning, consent-manager readiness
  • HIPAA — a BAA covering us and every sub-processor; PHI never leaves a BAA-covered endpoint
  • NHS DTAC — clinical-safety case (DCB0129/0160), DSPT alignment, interoperability and accessibility evidence

Compliance posture on every engagement

  • ISO/IEC 27001, 9001, 20000 and 42001 certified management systems
  • Sub-processor list published per engagement
  • Full output audit log, exportable to your SIEM
  • Human review checkpoints designed in, not bolted on
  • OWASP LLM Top 10 controls, PII masking, permission-aware retrieval

Choosing the tool.

Prompting, retrieval or fine-tuning. Rules, agents or workflows. We say which, and why.

  1. 01Retrieval by default: grounded, citable, and current the day your documents change. Fine-tuning only when a task needs a style or format retrieval cannot reach.
  2. 02Rules where the process is deterministic, a durable workflow engine (Temporal, Step Functions) to orchestrate, AI only for the judgement steps, with a person in the loop.
  3. 03Hyperscaler document AI for forms, AI-native parsers for complex tables, self-hosted Docling or Unstructured when documents cannot leave the network.
  4. 04If a copilot or a rule does the job, we will say so. It is cheaper to run and easier to govern.

FAQ

Questions?
We're here to help.

Straight answers on data, models, accuracy and ownership. Still have a question? We're just a message away.

Get in touch
  1. 01Is this retrieval-augmented generation, fine-tuning, or something else?
    Usually retrieval-augmented generation over your own documents, because it keeps answers grounded and citable and it changes the day your documents change. We fine-tune when a task needs a style or a format the base model cannot reach with retrieval alone, and we say which we recommend and why in the discovery brief.
  2. 02How do you measure whether it works?
    An eval set is agreed in discovery, with the accuracy threshold written down before anything is built. The pilot is measured against it, and the same set runs as a regression test on production traffic after go-live, so a model update that lowers accuracy fails a test rather than a customer.
  3. 03Can our documents stay in India?
    Yes. We deploy into your own AWS, Azure or Google Cloud account in an India region, handle personal data under the DPDP Act, and can run open-source models inside that account when documents cannot be sent to a model provider.
  4. 04What happens when the model is unsure?
    Every extracted field carries a confidence score. Fields below the threshold go to a review queue where a person sees the field, the source region and the score, and decides. Nothing is written back to your system of record until the check has passed or a person has approved it.
  5. 05Do you work with the systems we already have?
    That is the point of the line. The model is the smaller part; the integration with your ERP, LIS, CRM or core system through its API, and the audit log around it, is where the work is. Nothing is replaced to make room for AI.
  6. 06Which model providers see our data, and can we choose?
    You choose. The routing table is a deliverable: public data may go to a hosted model; personal, clinical or financial data goes to an in-account open-weights model or a DPA/BAA-covered endpoint.
  7. 07How do you measure false positives and false negatives?
    Against the golden set agreed in discovery, per field or per class, with the thresholds written down. The same set runs on production traffic weekly.
  8. 08What happens when a model update changes behaviour?
    Versions are pinned. An update runs shadow against the eval set before it replaces anything, and you are told what changed.
  9. 09Who maintains the connectors when our ERP changes?
    Under the operations retainer, we do; otherwise the runbook and the contract tests are yours and your team can.
  10. 10Can this run on-premises or in a private subnet?
    Yes, with open-weights models and self-hosted parsers (Docling, Unstructured) where documents cannot leave the network.
  11. 11Does the EU AI Act apply to us?
    If you sell into the EU and the use is high-risk (credit scoring, insurance risk assessment, clinical decision support, hiring), deployer duties applied from August 2026. We produce the oversight design, logs and notices they require.
  12. 12What do we own at the end?
    Everything: code, prompts, eval sets, models, infrastructure code and logs, in your repositories and accounts from day one.

From the work on this line

Real problems. Real outcomes.

View all insights

Have an AI automation project in mind?

Book a 30-minute discovery call. We'll review the workflow and tell you honestly whether AI is the right tool for it.

Talk to an AI engineerEmail hello@orbitnexa.com
+91 912-195-7728Hyderabad, IndiaEvery brief gets a senior review. Reply within 1 business hour, 9 AM-7 PM IST.