We design and build AI agents that plan, decide, and act across documents, systems, and multi-step workflows, built by senior engineers. Production-grade architecture, evals and guardrails from day one, and full ownership at handover.
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80+
Workflows Automated
60+
Engineers In-House
96%
Client Retention
Built for teams at a specific inflection point.
Make the right call, before you pick a framework.
Most teams pick a tool before they understand the problem shape. Here is the honest comparison across the dimensions that determine whether it is still running well eighteen months from now.
| What matters |
Recommended
Custom build
|
Common alternative
Off-the-shelf SaaS
|
Other path
Low-code platform
|
|---|---|---|---|
| Handles unstructured inputs | Documents, emails, images, and voice natively | Structured data and fixed UI paths only | Text input only, constrained tool use |
| Multi-step reasoning | Plans across steps, calls tools, recovers from errors | Executes fixed sequences | Single-turn or shallow multi-turn |
| Adapts to change | Models retrain; agent logic updates without full rebuilds | Requires a rebuild when the format or UI changes | Prompt-dependent, performance degrades over time |
| Integration depth | Calls APIs, reads DBs, writes to systems, triggers webhooks | Scripted connectors, constrained error handling | Requires custom middleware for system access |
| Exception handling | Configurable fallback and human-in-the-loop escalation | Routes to the manual queue | Needs a human review queue bolted on separately |
| Auditability | Full decision trace, confidence scores, reason logging | Step log exists, readable only by developers | Requires custom instrumentation for traceability |
| Time to first value | 4 to 8 weeks for the first production agent | 6 to 12 weeks for a stable bot | Days to prototype, weeks to production-quality |
| Long-term cost (3 yrs) | Flat infrastructure plus improvement sprints | Per-bot licensing grows with process count | Prompt engineering overhead grows with scale |
From a single scoped agent to a coordinated multi-agent platform, we design, build, and operate across the full spectrum. Start where the value is clearest and expand from there.
A custom agent built around one workflow, with scoped decisions, defined tool calls, and guardrails from the start.
Single-agentMulti-step reasoningTool use
A custom agent built around a specific workflow: a scoped decision surface, defined tool calls, confidence thresholds, guardrails, and fallback logic from the start. Tested on your actual documents, in your actual environment.
An honest read on whether your process is a strong fit for agents, and what production-viable actually takes.
FeasibilityArchitectureRoadmap
We assess whether your target process is a strong candidate for agentic AI, what architecture makes it production-viable, and what it realistically takes to build. You get a prioritised roadmap with honest effort estimates before any build commitment.
An orchestration layer for processes that span finance, legal, support, and vendor data.
Agent orchestrationLangGraphCrewAI
When a process spans finance, legal, support, and vendor data, one agent is rarely enough. We design the full orchestration layer: handoffs, context sharing, escalation paths, and system-level performance, with defined boundaries and observable behaviour for every agent in the network.
Agents that interpret instructions, handle variation, retry intelligently, and ask when a real decision is needed.
End-to-end executionEvent-drivenHuman-in-the-loop
Agents that interpret instructions, handle variation, retry intelligently, and surface the right information when a genuine decision is needed. The manual steps in your process are replaced by agents that understand the workflow's intent and handle the full range of cases it encounters.
The connection layer that lets agents read and write across your CRM, ERP, and legacy systems.
REST & webhookCRMERPLegacy connectors
An agent is only as useful as the systems it can read and write. We build the connection layer across CRM, ERP, HRMS, document stores, and third-party APIs, with retry logic, schema validation, and monitoring. Modern SaaS stack or legacy on-prem, integration complexity is scoped before the build begins.
A memory layer that grounds every agent output in your real policies, contracts, and documentation.
Vector storesRetrieval pipelinesGrounded outputs
Agents operating on your policies, contracts, and documentation need a memory layer that is accurate and auditable. We build RAG pipelines that ground outputs in your actual knowledge base: chunking, embedding, retrieval tuning, and citation tracking, so every response traces back to a source.
Start with the workflow that costs you the most.Tell us what it is. We will show you what an agent can do with it.
What we have built, across categories.
Different process shapes, same engineering discipline. Whatever the category, you get the same senior team, the same production rigour, and full ownership at handover.
Give your operations team back the hours they spend on coordination.
Read inbound invoices, contracts, forms, and claims, extract fields, validate against your business rules, then route or escalate based on findings. Works across PDF, scanned image, and email attachment.
Gather context, apply routing logic, track deadlines, and escalate when a genuine decision is needed, every step moving and every stakeholder informed.
Pull data from multiple systems, match records, flag discrepancies, and post to the right destination. The weekly reconciliation ritual runs automatically, with a review queue for cases that warrant attention.
Where accuracy and auditability are essential at every step.
Extraction, three-way match, exception routing, and ERP posting with a complete audit trail and a human review queue for the cases that need it.
Continuous checks against policy thresholds, regulatory deadlines, and data quality rules. Structured alerts for what needs attention, and reports your auditors will actually use.
Key terms, obligation dates, renewal triggers, and risk flags extracted from contracts as structured output for your CLM or downstream systems, with a confidence score on every field.
Faster responses, smarter routing, fewer handoffs.
Classify inbound requests by intent, urgency, and product area, route to the right team, trigger a resolution, or draft a response for review. Every query handled with speed and context.
Enrich incoming leads with firmographic and intent data, score against your ICP, and route to sales with context, cutting the time between inquiry and a first meaningful conversation.
Status updates, return initiation, and refund triggers that run automatically. The agent acts where it can and flags the cases that need a human, keeping customers informed throughout.
Agents for every vertical.
How we build, every step of the way.
Evals, observability, fallback logic, and defined performance criteria before anything goes near production. Here is how that looks in practice.
Before a line of code is written, we define what the agent decides autonomously, what it escalates, and what it logs for review. Every agent has a confidence threshold, a human-in-the-loop path, and a clear definition of when it stops and asks.
Every agent ships with a test set from your actual documents, a baseline accuracy target agreed up front, and a confidence-based review queue. You know the expected performance on your data before production code is written.
Every connector surfaces retry counts, error rates, and schema violations in a monitoring layer. Integration health is visible to your team at all times, and issues are caught and resolved before they reach downstream systems.
We ship with evaluation harnesses and review triggers, so you know when an agent's logic or model needs updating well before any accuracy change is visible to end users.
Every production agent is reviewed for accuracy, edge-case behaviour, and robustness before go-live. Every deployment includes a tested fallback and a human escalation path.
We assess the workflow before recommending a framework. The architecture follows the process, so the agent is scoped correctly the first time and ships into production ready to perform.
Your prompt logic, credentials, vector store, and infrastructure all sit on open, portable foundations. Documented and yours to run, extend, or hand to a new team independently.
Accuracy benchmarks, defined fallback behaviour, and observability from day one, so your team always knows how the agent is performing before anything reaches a customer.
Agent systems scoped and built by engineers who have shipped production AI. The people who assess your workflow are the people who build it.
Selected work Real outcomes.
Selected agentic AI projects across financial services, logistics, healthcare, and enterprise operations.
View All Case StudiesOur achievements display our capabilities
Trusted voices. Real outcomes.
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
A four-phase model that delivers production-ready agentic systems on a real timeline, starting with the workflow, then the architecture, then the agent.
Book a call{ 01 }· 1 to 2 weeks
We map inputs, outputs, decision points, integration touchpoints, and volume. You walk away with a prioritised agent scope, a reference architecture, realistic accuracy expectations, and a firm timeline before any build commitment.
{ 02 }· 2-week sprints
Extraction pipelines, reasoning logic, tool integrations, and the monitoring layer, built in agile sprints with a working demo every two weeks, deployed to a staging environment that mirrors production.
{ 03 }· Before go-live
We run the agent against your real data, measure accuracy against agreed benchmarks, conduct UAT, and resolve edge cases before production traffic hits. Every edge case has a defined fallback before go-live.
{ 04 }· Launch and ongoing
Staged rollout, real-time monitoring, on-call handover, and a post-launch review cycle. Model tuning and logic improvements based on production signals, backed by an SLA you can plan against.
Engagement models, built to fit.
Senior AI engineers in your workflow from week one. Choose the shape that fits your scope, your team, and your timeline.
A scoped build with a defined workflow, integration set, and acceptance criteria. Fixed price, a realistic timeline, and a working production system you own at handover.
A senior AI pod embedded in your tools and rituals, running two-week sprints. Ongoing agent builds, integration, optimisation, and new workflow development as your operations evolve.
AI systems that plan across multiple steps, call external tools, make decisions based on context, and operate autonomously toward a defined goal. Where a chatbot answers a question, an AI agent runs a process, handling variation, recovering mid-task, and adapting as it goes.
RPA executes fixed sequences on structured data reliably for stable, high-volume processes, and requires rebuilding when formats change or exceptions arise. An AI agent handles the full process, including variable cases: it reads a PDF, extracts fields, looks up a policy, makes a routing decision, and posts to your ERP, as one autonomous workflow.
High-volume processes with unstructured or semi-structured inputs that follow consistent logic even when the content varies. Document intake, approval routing, query triage, compliance checking, lead qualification, and reconciliation are all strong fits. If your team spends real hours on something that follows a pattern, it is worth assessing.
A focused single-agent build typically takes four to eight weeks from assessment to production. A multi-agent system runs eight to sixteen weeks. The assessment phase gives you a firm timeline and realistic accuracy expectations before any build commitment.
You do, fully. Agent definitions, prompt logic, vector stores, integration credentials, and cloud infrastructure all live in your accounts on open, portable foundations your team can operate, modify, and extend independently.
Every agent ships with a defined accuracy benchmark, a test set from your real data, and a confidence-based human review queue. The system knows its confidence level on every output and routes low-certainty cases to a human automatically.
Every agent has a fallback path, a confidence threshold below which it escalates rather than acts, and a full decision trace. All outputs are visible in the monitoring layer, so your team maintains full visibility into agent behaviour at all times.
Yes. Most clients have at least one legacy system in the chain: an on-prem ERP, an older API, or a system that predates modern integration standards. We build the integration layer around what exists, using API connectors, database-level access, file-based pipelines, or screen capture. Integration complexity is scoped accurately before the build begins.
Cost transparency, before the call.
We put numbers on the page. Here is the honest band by engagement type, plus the five variables that move the number once we scope your workflow.
| Tier comparison | Single workflow Focused Agent $15K - $45K ₹12L - ₹36L |
Most chosen
Most chosen Production Agent System $45K - $160K ₹36L - ₹1.3Cr | Series B and beyond Enterprise Agentic Platform $160K - $500K ₹1.3Cr - ₹4Cr | Ongoing capacity Embedded Pod from $18K / month from ₹15L / month |
|---|---|---|---|---|
| Timeline | 4 to 8 weeks | 8 to 16 weeks | 4 to 9 months | Sprint-by-sprint |
| What you get |
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Each agent needs its own process mapping, logic, test cases, and integration work.
One consistent document type is simpler than ten variants across formats, languages, and layouts.
Every system in the chain adds scoping, authentication, error handling, and contract testing.
99% extraction accuracy needs more training data and tuning cycles than a system where 90% is production-viable.
Simple routing is straightforward. Configurable review queues, escalation paths, and override logging take additional design and build.
Bands cover discovery, engineering, integrations, evals, and PM. Model and inference fees, hosting, and third-party licenses are billed at cost.
Tell us the process, the volume, and where you want to go further. A senior AI engineer replies within one working day. Direct conversation, real answers, a real plan.
Step 1 - Tell us where you are
Which workflow costs you the most time or carries the most risk. We sign an NDA before any specifics.
Step 2 - Speak to an agent engineer
A senior AI engineer joins within two working days to map your process, your integration landscape, and the shortest path to a working agent.
Step 3 - Get a real plan
A workflow architecture, a scope band, and an accuracy benchmark you can plan against, plus a production system built to last.
Tell us the process, the volume, and where you want to go further. A senior AI engineer replies within one working day. Direct conversation, real answers, a real plan.
