Services we provide
We build the parts that need to fit your workflow: agents, vision, private search, forecasts, monitoring, and the handoff to your team.
Workflow agents
Agents that complete repeatable work across tools, follow business rules, check their output, and hand off when a person needs to decide.
Useful when a process depends on the same steps happening across email, portals, spreadsheets, CRMs, ticketing systems, or internal tools.
- Create a case from an email and attach the right files.
- Update records across two systems after approval.
- Prepare a daily work queue for operations staff.
Browser and ERP agents
Automation for systems that do not have clean APIs, using the screens and workflows your team already relies on.
Useful when important work still happens inside legacy portals, vendor systems, ERP screens, or admin tools that are hard to integrate directly.
- Move approved entries from one portal into another system.
- Check status fields across vendor or customer portals.
- Submit routine forms after a manager approves the data.
Document processing agents
Agents that read documents, extract fields, validate the result, and route the work with confidence scores and review paths.
Useful when teams spend time opening PDFs, scans, emails, forms, invoices, contracts, reports, or attachments before taking the next action.
- Extract key fields from incoming documents.
- Compare a submitted form against internal records.
- Send low confidence or mismatched items to review.
Multi-agent orchestration
Coordinated agents with separate roles for research, drafting, checking, routing, and approval.
Useful when one automated step is not enough, and the work needs multiple checks before it can be trusted in production.
- One agent prepares a draft, another checks it against source material.
- A reviewer agent flags missing data before submission.
- A routing agent sends each case to the right owner.
Human review flows
Review queues and approval paths for work that needs judgment, exception handling, or accountability.
Useful when the system can do most of the work but certain cases still need a person for accuracy, compliance, customer sensitivity, or business risk.
- Route high value actions for approval.
- Send uncertain outputs to a subject matter expert.
- Keep a record of who approved, changed, or rejected an action.
Automation discovery
A practical review of where automation will actually help, what data is available, and what should stay manual.
Useful before investing in a build, especially when teams have many manual processes but no clear priority order.
- Map repeated handoffs across a department.
- Rank use cases by value, effort, and risk.
- Identify process gaps that should be fixed before automation.
Private LLM deployment
Language models deployed inside the environment you choose, with clear boundaries around data, access, and retention.
Useful when teams want LLM capability but cannot send sensitive information into unmanaged public tools.
- Run an internal assistant inside a private cloud environment.
- Keep prompts and documents within a controlled network.
- Use open source or hosted models with enterprise controls.
Private RAG systems
Question answering grounded in your approved documents, with citations so people can verify the answer before acting.
Useful when staff need fast answers from policies, contracts, manuals, SOPs, tickets, reports, or internal knowledge bases.
- Answer questions with links to the source document.
- Search current policy instead of old downloaded copies.
- Limit results based on team, role, or permission.
Semantic search
Search that understands meaning and context, not just exact keywords.
Useful when employees cannot find information because the same issue is described in different words across documents and systems.
- Find past cases that describe a similar problem.
- Search notes, tickets, PDFs, and records together.
- Rank results by business relevance, not only keyword match.
Enterprise chat assistants
Assistants that answer routine questions, collect context, and route requests to the right system or team.
Useful when internal teams receive repeated questions and requests that can be handled with approved knowledge and clear escalation.
- Answer policy or process questions for employees.
- Collect missing information before opening a ticket.
- Summarize a request before sending it to the right owner.
Embedding and reranker tuning
Retrieval tuning so the right information appears first and irrelevant results are pushed down.
Useful when a search or RAG system works in demos but misses the exact language, abbreviations, and document types your company uses.
- Improve search quality for internal terminology.
- Prioritize current documents over outdated versions.
- Reduce irrelevant answers from broad knowledge bases.
Summarization and briefing systems
Structured summaries from long documents, meetings, tickets, calls, or account history.
Useful when teams need a reliable brief before making a decision, calling a customer, approving work, or escalating an issue.
- Summarize long documents into review notes.
- Prepare account briefs from CRM and support history.
- Turn meeting notes into actions, risks, and owners.
Custom ML model training
Models trained on your data for classification, prediction, ranking, scoring, or detection tasks.
Useful when off the shelf models do not match your business rules, data patterns, accuracy target, or decision process.
- Predict risk, demand, churn, delay, or failure.
- Classify incoming work into the right category.
- Rank records, leads, products, or cases by priority.
LLM fine-tuning
Adapt language models to your domain, tone, output format, and quality bar.
Useful when prompting alone cannot produce consistent outputs, especially for repeated drafting, extraction, classification, or structured response tasks.
- Match company writing style and terminology.
- Improve consistency for repeated document tasks.
- Teach a model a specific response format.
Training data engineering
Clean, label, enrich, and structure the data needed for reliable model training and evaluation.
Useful when the raw data exists but is scattered, inconsistent, incomplete, or not ready for modeling.
- Turn historical records into a labeled dataset.
- Create annotation guidelines for reviewers.
- Remove duplicates, bad labels, and unusable records.
Synthetic data generation
Create realistic training or test data when real examples are limited, sensitive, expensive, or imbalanced.
Useful when a model needs to learn rare cases or when teams need safe test data without exposing sensitive information.
- Generate edge cases for model testing.
- Balance underrepresented classes in a dataset.
- Create privacy safe samples for development.
Model evaluation
Evaluation sets, scoring workflows, regression checks, and review loops that show whether a model is ready to use.
Useful when a model needs to be measured against real business outcomes, not only a benchmark score.
- Test a model before each release.
- Compare model versions on the same cases.
- Track accuracy, consistency, and failure modes.
Model distillation and compression
Smaller and faster models for production use, built to reduce cost and latency while preserving enough quality.
Useful when a working model is too slow, too expensive, or too large to run where the business needs it.
- Reduce inference cost for high volume workflows.
- Run a smaller model in a private environment.
- Improve response time for user facing tools.
Visual quality inspection
Vision systems that identify defects, mismatches, missing items, quality issues, or process deviations.
Useful when visual checks are repetitive, time sensitive, hard to scale, or inconsistent across shifts and locations.
- Flag items that need manual inspection.
- Check labels, packaging, placement, or assembly.
- Track quality trends over time.
OCR and document vision
Document vision for forms, scans, handwriting, tables, screenshots, and mixed layouts.
Useful when standard OCR is not enough because documents vary by vendor, customer, location, or department.
- Read scanned forms and route them to the right workflow.
- Extract tables from PDFs or images.
- Match document fields to existing records.
Video analytics
Systems that detect objects, events, movement, activity, or safety conditions from video streams.
Useful when camera footage already exists but teams need signals, alerts, counts, or evidence instead of hours of manual review.
- Detect events that require attention.
- Count activity across locations or time windows.
- Create searchable clips from long video footage.
Medical imaging support
Vision models for detection, segmentation, measurement, and review support in specialist workflows.
Useful when experts need help organizing images, highlighting regions, measuring patterns, or prioritizing review without removing human judgment.
- Pre mark regions for expert review.
- Measure changes across image sets.
- Prioritize cases that need faster attention.
Edge model deployment
Models that run close to the machine, camera, device, or site where the decision happens.
Useful when latency, bandwidth, privacy, or uptime requirements make cloud only processing a poor fit.
- Run inspection locally when network access is unreliable.
- Process sensitive images without sending them out.
- Make real time decisions near equipment or cameras.
Sensor and perception systems
Systems that combine cameras, sensors, telemetry, and business data into usable operational signals.
Useful when a single data source is not reliable enough and decisions need a fuller picture of what is happening.
- Combine camera events with equipment data.
- Turn raw telemetry into status signals.
- Create alerts from multiple weak signals.
Forecasting
Forecasting models for demand, workload, risk, volume, inventory, staffing, or capacity.
Useful when planning depends on signals that change over time and teams need a range they can trust, not a single magic number.
- Forecast demand by product, region, team, or channel.
- Predict workload before staffing decisions.
- Estimate future risk or volume with confidence bands.
Predictive maintenance
Models that detect early signs of failure from sensor data, logs, inspections, and maintenance history.
Useful when downtime is expensive and teams need to move from reactive repairs to planned intervention.
- Flag assets that need inspection soon.
- Prioritize maintenance based on risk.
- Combine sensor trends with service history.
Fraud and anomaly detection
Detection systems that surface unusual behavior, records, transactions, usage, or operational patterns.
Useful when teams need to review the unusual without being overwhelmed by false alarms.
- Flag suspicious activity for investigation.
- Detect unusual changes in operational data.
- Prioritize anomalies by severity and confidence.
Recommendations
Recommendation systems for next best actions, products, content, support steps, or internal decisions.
Useful when teams or customers face too many options and need relevant suggestions based on behavior and context.
- Suggest the next action for a customer or case.
- Recommend relevant products, content, or documents.
- Prioritize follow ups for sales, support, or operations.
Routing and scheduling optimization
Optimization models for assigning people, jobs, inventory, vehicles, tasks, or resources under real constraints.
Useful when manual planning is slow, inconsistent, or unable to account for all constraints at once.
- Assign work based on skills, priority, and capacity.
- Plan routes or schedules around time windows.
- Rebalance queues when demand changes.
Dynamic pricing and planning
Scenario models for pricing, supply planning, capacity decisions, and demand response.
Useful when pricing or planning decisions need to respond to demand, inventory, timing, margin, and risk.
- Compare planning scenarios before committing.
- Adjust prices or offers within approved guardrails.
- Model supply and demand tradeoffs.
MLOps and model deployment
Production paths for model versioning, release, rollback, monitoring, and ownership.
Useful when a model is moving from a notebook or prototype into a system that real teams depend on.
- Release models through staged environments.
- Rollback a bad model version quickly.
- Track who changed, approved, and deployed a model.
Data pipelines
Reliable ingestion, transformation, validation, and delivery of data for AI systems.
Useful when model quality depends on data that comes from multiple tools, teams, formats, or schedules.
- Validate data before it reaches a model.
- Join records from operational systems.
- Detect missing, delayed, or changed source data.
Model monitoring
Monitoring for quality, drift, latency, cost, usage, and incidents after launch.
Useful when teams need to know whether an AI system is still working as expected after real users and new data arrive.
- Alert when accuracy starts to move.
- Track response time and model cost.
- Watch input patterns for drift.
Evaluation pipelines
Automated checks that test model behavior before changes reach production.
Useful when prompts, models, data, or code change often and the team needs a repeatable way to catch regressions.
- Run test cases before each release.
- Block changes that fail critical examples.
- Compare outputs across model versions.
Cloud and private environment setup
Infrastructure setup for cloud, VPC, on premises, or private deployments.
Useful when AI systems need to match an existing security, compliance, networking, or procurement environment.
- Deploy inside a private cloud boundary.
- Set up secure model and data access.
- Separate development, staging, and production environments.
API and system integration
Connect AI systems to the tools where employees already work.
Useful when a model is only valuable if it can read from and write back to CRMs, ERPs, warehouses, portals, ticketing tools, or internal apps.
- Show predictions inside an existing workflow.
- Push approved outputs back into the system of record.
- Trigger automation from a business event.
DPDPA readiness
Architecture, records, and operating controls for responsible AI under India’s DPDP Act.
Useful when AI systems touch personal data and the company needs clear evidence of how data is collected, used, retained, and protected.
- Map data flows for an AI workflow.
- Document consent, retention, and access rules.
- Prepare evidence for internal or external review.
Access control and audit logs
Permissions, traceability, and reviewable records for model actions and data access.
Useful when different teams should see different data, and every important action needs a clear history.
- Restrict answers based on user role.
- Log each agent action and approval.
- Create audit trails for sensitive workflows.
Bias and fairness review
Practical review of model outcomes across the groups, regions, products, or cases that matter for the business.
Useful when model decisions affect people, customers, employees, vendors, or access to important services.
- Measure outcome differences across segments.
- Review training data for missing groups.
- Adjust thresholds or process rules where needed.
Explainability
Model outputs with reasons that people can inspect, challenge, and use in decisions.
Useful when staff, customers, regulators, or managers need to understand why a system recommended an action.
- Show the main factors behind a score.
- Attach source evidence to a recommendation.
- Make decisions easier to review and challenge.
Red teaming
Structured testing for leakage, abuse, prompt injection, jailbreaks, and unsafe behavior.
Useful before launching assistants, agents, or public facing AI systems that can access tools, documents, or sensitive context.
- Test whether private data can be exposed.
- Try prompt injection attacks against workflows.
- Find unsafe outputs before launch.
AI policy and risk controls
Practical rules for AI use, approvals, vendors, data handling, and model behavior.
Useful when teams are already experimenting with AI and leadership needs a clear operating model without slowing everyone down.
- Define which tools can be used for which data.
- Create approval paths for new AI use cases.
- Set review rules for sensitive outputs.
AI readiness audit
A grounded review of workflow fit, data quality, risk, ownership, and likely return.
Useful when leadership wants to move into AI but needs a clear view of what is ready, what is not, and what to fix first.
- Review data, tools, and process maturity.
- Identify blockers before a build starts.
- Prioritize the first realistic AI project.
Use-case discovery
Find and rank AI opportunities by business value, effort, risk, and operational fit.
Useful when a company has many AI ideas but no objective way to choose the first few to build.
- Interview teams and map repeated work.
- Score use cases by impact and feasibility.
- Create a shortlist for leadership review.
AI roadmap
A practical plan for sequencing builds, dependencies, teams, budgets, and ownership.
Useful when AI needs to become an operating capability, not a series of disconnected experiments.
- Plan the first 90 days of AI work.
- Sequence data, integration, and model work.
- Assign owners for build, review, and adoption.
ROI and feasibility assessment
Estimate value, cost, data needs, integration effort, and implementation risk before committing budget.
Useful when teams need to decide whether an AI idea is worth building, buying, delaying, or dropping.
- Estimate savings or revenue impact.
- Compare build versus buy options.
- Identify assumptions that need proof first.
Team enablement
Training, documentation, operating guides, and review practices so your team can own the system.
Useful when adoption matters as much as the build, and users need to trust, review, and improve the AI system over time.
- Train reviewers and operators.
- Write playbooks for common issues.
- Teach teams how to monitor outputs safely.
Fractional AI partner
Ongoing senior AI guidance for architecture, strategy, vendor review, hiring, and delivery decisions.
Useful when a company needs experienced AI judgment but is not ready to hire a full internal leadership team.
- Review vendor proposals and architecture choices.
- Support internal teams during delivery.
- Help leadership decide what to build next.