Models chosen per task, then measured.
We build document AI, retrieval and agentic workflows on hosted and open-source models, with evaluation sets, guardrails and data residency decided up front.
Powerful, probabilistic and auditable only by design.
Large language models and document AI services handle the work rule-based bots cannot: free-text emails, variable documents, contract clauses, ticket classification and multi-step reasoning with tools. Paired with RPA and APIs, they move processes from partial to near straight-through.
They are a poor fit where a deterministic rule already works, where every output must be exactly reproducible, or where nobody owns the evaluation. We choose models per task on accuracy, latency, cost and residency. We keep prompts under version control, test every change against a golden dataset, and put a human in the loop wherever confidence drops.
Classification, extraction, summarisation and grounded question answering over your content.
Agents that take actions in systems of record, which need tight tool permissions.
Replacing a reliable deterministic rule with a model call.
What we build on the AI stack.
Document AI pipelines
OCR and layout extraction with Azure AI Document Intelligence, AWS Textract or Google Document AI, then LLM normalisation and validation rules.
01Email and ticket triage
Classification, entity extraction and routing with structured JSON outputs validated against a schema before any system update.
02Retrieval-augmented assistants
Chunking, embedding and hybrid search over policies, SOPs and contracts, with citations returned for every answer.
03Tool-calling agents
Agents that call APIs, RPA bots and database queries as tools, with scoped permissions, step limits and approval gates.
04Evaluation harnesses
Golden datasets, field-level accuracy scoring, model-graded checks and regression runs on every prompt or model change.
05Private model deployments
Open-source models served inside your cloud tenancy or data centre when data cannot leave a defined boundary.
06The AI components we work with.
Hosted models
Document AI
Open-source and serving
Retrieval and orchestration
Evaluation and guardrails
Standards we apply on the AI stack.
- Data residency, retention and provider data-use terms agreed before any data moves
- PII and sensitive fields masked or tokenised before prompts leave your boundary
- Prompts, model versions and parameters held in version control and released like code
- Every output parsed against a schema, with failures retried or routed to a person
- Golden datasets built from real, labelled samples and rerun on every change
- Confidence thresholds per field, with human-in-the-loop review below them
- Agents limited to named tools, scoped credentials and a maximum step count
- Full traces of inputs, retrieved context, tool calls and outputs kept for audit
- Cost and latency budgets tracked per transaction, with model fallbacks defined
Evaluation comes before scale.
Frame
Define the task, the decision it supports, acceptable error and residency constraints.
Sample and label
Collect representative inputs and label a golden dataset with your subject experts.
Prototype and compare
Test two or three model and prompt options against the dataset on accuracy, cost and latency.
Harden
Add guardrails, schema validation, review queues, tracing and integration with bots or APIs.
Operate
Monitor drift and exception rates, rerun evaluations and update prompts under change control.
Which model do you recommend?
It depends on the task. We benchmark candidates on your labelled data and recommend the one that meets accuracy, latency, cost and residency targets together.
Will our data be used to train public models?
We select providers and deployment options whose enterprise terms address training and retention, and confirm those terms with you in writing before go-live.
Can the data stay in India or a specific region?
Often, yes. We use regional deployments of hosted models where available, or serve open-source models inside your tenancy when data must not leave.
How do you stop hallucinations reaching our systems?
Answers are grounded in retrieved sources with citations, outputs are schema-validated, and low-confidence results go to a human before any write.
Find your first ten automations in two weeks.
A structured discovery sprint that maps your processes, scores them for automation potential and hands you a prioritised roadmap with effort and payback for each.