Flagship Service

Agents that work.
Not agents that demo.

We design, build, and harden autonomous AI agents and multi-agent pipelines that operate reliably in production - not just in controlled environments.

Start a project See case studies

Reality check

What a real agent actually is

What people think

-A chatbot with a longer system prompt
-Automation that follows a fixed script
-A single LLM call with tools attached
-Works if the demo works

What production agents do

-Decompose goals into dynamic subtasks
-Recover from failures and retry with new strategies
-Coordinate across tools, APIs, and data sources
-Operate within defined guardrails autonomously

What makes ours different

-Evaluation harness before any deployment
-Observability from day one - every step logged
-Fallback paths for every critical action
-Hardened for edge cases, not just happy paths

What we build

Agent types we build

Workflow Agents

Automate multi-step business processes end-to-end with branching logic and approval gates.

Research Agents

Gather, synthesise, and deliver structured intelligence from the web, documents, and APIs.

Code Agents

Write, review, test, and deploy code autonomously - integrated into your CI/CD pipeline.

Data Agents

Query, clean, transform, and report on data in real time across warehouses and databases.

Customer-facing Agents

Handle support, onboarding, and sales conversations with context-aware reasoning.

How agents are wired together

Agent architecture patterns

The right architecture depends on task complexity, speed requirements, and how many steps are involved.

Simple tasks

Single agent

One LLM with a defined tool set handles a single, well-scoped task from start to finish.

User request

→ LLM reasons

→ Tool calls

→ Result

e.g. Document Q&A, form filling, data lookups

Multi-step workflows

Sequential pipeline

Agents are chained - each agent's output is the next agent's input. Deterministic ordering, easy to debug.

Input

→ Agent A (extract)

→ Agent B (classify)

→ Agent C (output)

e.g. Contract review → risk scoring → summary generation

Scale & speed

Parallel agents

Multiple agents run concurrently, coordinated by an orchestrator that merges results. 10× throughput.

Orchestrator

→ Agent 1 + Agent 2 + Agent 3 (parallel)

→ Merge

→ Output

e.g. Multi-source research, batch document processing

Industry applications

Use cases by industry

Healthcare

Clinical triage routing

Agent reads patient intake forms and routes cases to the right specialist queue - automatically.

Finance

Document extraction

Extracts, validates, and reconciles data across PDFs, spreadsheets, and banking APIs.

SaaS

Autonomous support

Resolves 80%+ of support tickets without human touch. Escalates when genuinely uncertain.

Logistics

Demand replanning

Monitors supply signals and replans demand forecasts in real time, surfacing actionable alerts.

E-commerce

Merchandising agent

Adjusts pricing, promotions, and product copy based on competitor signals and inventory levels.

Media

Content pipeline agent

Researches, drafts, formats, and schedules content across channels from a single brief.

What agents connect to

Tools & integrations

Agents are only as powerful as the tools they can use. Here's what we connect them to.

Web browsingCode executionREST APIsWebhooksSQL databasesVector storesEmailCalendarSlack / TeamsCRM (Salesforce, HubSpot)File systemsGitHubPDF / DOCX parsingSpreadsheetsZapier / Make

Need a custom tool? We write tool definitions and connection adapters for any API or internal system. If it has an endpoint, the agent can call it.

Our process

How we build agents

Problem scoping

Define what the agent must decide, what tools it needs, and what failure looks like.

Tool design

Design every tool the agent will call - typed inputs, deterministic outputs, error surfaces.

Orchestration

Wire together the reasoning loop, memory, routing logic, and sub-agent coordination.

Evaluation harness

Build a suite of golden cases. No agent ships without measurable pass rates.

Production hardening

Add retries, circuit breakers, cost limits, logging, and alerting before go-live.

Autonomy with accountability

Human-in-the-loop controls

Autonomy doesn't mean unaccountable. Every agent we build has explicit human oversight mechanisms.

Approval gates

High-stakes actions (sending emails, making payments, modifying records) pause and require explicit human sign-off before proceeding.

Confidence thresholds

Agents declare confidence scores. Below threshold, the task is routed to a human queue instead of auto-executing.

Audit logging

Every tool call, decision branch, and output is logged with timestamps and inputs. Replay any agent run for compliance review.

Rollback mechanisms

Actions are transactional where possible. Failed or flagged runs can be reversed without manual intervention.

Technology

Our agent stack

Orchestration

LangChainLLLlamaIndexCRCrewAIAUAutoGen

Models

GPTClaude SonnetGemini ProLlama

Memory

RedisPIPineconepgvectorMEMem0

Observability

LangSmithHEHeliconeCUCustom eval pipelinesOPOpenTelemetry

Measured, not guessed

Observability

You can't manage what you can't measure. Every agent ships with a full observability stack.

Agent run dashboard - live metrics

Token usage per run

avg 2,840 tokens / task

↓ 12% after prompt optimization

Tool call success rate

98.3% successful

↑ vs 91% at launch

Task completion rate

94.1% fully automated

5.9% escalated to human

Avg latency per step

1.2s per tool call

p95: 3.8s

Resource per run

0.041 avg

Target: <0.05

Escalation reasons

Top: low confidence (62%)

Ambiguous input (21%), API error (17%)

Agents in production

Legal tech

“Reduced contract review from 4 hours to 11 minuteswith a multi-agent legal analysis pipeline.”

94% of contracts now reviewed autonomously

Read case study

Customer operations

Agent handles 82% of tier-1 support tickets without human escalation.

82%

Auto-resolved

1.8s

Avg response

94%

CSAT score

60%

Efficiency gain

Read case study

FAQ

Common questions
about agent development.

A chatbot responds to messages. An agent takes actions - it can call APIs, run code, read and write files, send emails, and coordinate with other agents. The key difference is tool use and autonomous execution across multi-step tasks.

Every agent we build includes error handling, retry logic, and configurable fallback paths. For irreversible actions, we use transactional patterns. Failed or ambiguous runs are escalated to a human queue with full context attached.

Yes. We write custom tool adapters for internal databases, legacy REST services, and internal tooling. If your system has an endpoint, a CLI, or a webhook - we can make it a tool the agent can call.

A focused, well-scoped agent takes 4–6 weeks from discovery to production deployment. Complex multi-agent systems with many tool integrations typically run 8–12 weeks. We always ship a working prototype in week 2.

We're framework-pragmatic. We've shipped agents using LangGraph, CrewAI, OpenAI Assistants, and custom orchestration loops. The right choice depends on your latency requirements, debugging needs, and the complexity of your agentic graph.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Flagship Service

Agents that work.
Not agents that demo.

We design, build, and harden autonomous AI agents and multi-agent pipelines that operate reliably in production - not just in controlled environments.

Start a project See case studies

Reality check

What a real agent actually is

What people think

-A chatbot with a longer system prompt
-Automation that follows a fixed script
-A single LLM call with tools attached
-Works if the demo works

What production agents do

-Decompose goals into dynamic subtasks
-Recover from failures and retry with new strategies
-Coordinate across tools, APIs, and data sources
-Operate within defined guardrails autonomously

What makes ours different

-Evaluation harness before any deployment
-Observability from day one - every step logged
-Fallback paths for every critical action
-Hardened for edge cases, not just happy paths

What we build

Agent types we build

Workflow Agents

Automate multi-step business processes end-to-end with branching logic and approval gates.

Research Agents

Gather, synthesise, and deliver structured intelligence from the web, documents, and APIs.

Code Agents

Write, review, test, and deploy code autonomously - integrated into your CI/CD pipeline.

Data Agents

Query, clean, transform, and report on data in real time across warehouses and databases.

Customer-facing Agents

Handle support, onboarding, and sales conversations with context-aware reasoning.

How agents are wired together

Agent architecture patterns

The right architecture depends on task complexity, speed requirements, and how many steps are involved.

Simple tasks

Single agent

One LLM with a defined tool set handles a single, well-scoped task from start to finish.

User request

→ LLM reasons

→ Tool calls

→ Result

e.g. Document Q&A, form filling, data lookups

Multi-step workflows

Sequential pipeline

Agents are chained - each agent's output is the next agent's input. Deterministic ordering, easy to debug.

Input

→ Agent A (extract)

→ Agent B (classify)

→ Agent C (output)

e.g. Contract review → risk scoring → summary generation

Scale & speed

Parallel agents

Multiple agents run concurrently, coordinated by an orchestrator that merges results. 10× throughput.

Orchestrator

→ Agent 1 + Agent 2 + Agent 3 (parallel)

→ Merge

→ Output

e.g. Multi-source research, batch document processing

Industry applications

Use cases by industry

Healthcare

Clinical triage routing

Agent reads patient intake forms and routes cases to the right specialist queue - automatically.

Finance

Document extraction

Extracts, validates, and reconciles data across PDFs, spreadsheets, and banking APIs.

SaaS

Autonomous support

Resolves 80%+ of support tickets without human touch. Escalates when genuinely uncertain.

Logistics

Demand replanning

Monitors supply signals and replans demand forecasts in real time, surfacing actionable alerts.

E-commerce

Merchandising agent

Adjusts pricing, promotions, and product copy based on competitor signals and inventory levels.

Media

Content pipeline agent

Researches, drafts, formats, and schedules content across channels from a single brief.

What agents connect to

Tools & integrations

Agents are only as powerful as the tools they can use. Here's what we connect them to.

Web browsingCode executionREST APIsWebhooksSQL databasesVector storesEmailCalendarSlack / TeamsCRM (Salesforce, HubSpot)File systemsGitHubPDF / DOCX parsingSpreadsheetsZapier / Make

Need a custom tool? We write tool definitions and connection adapters for any API or internal system. If it has an endpoint, the agent can call it.

Our process

How we build agents

Problem scoping

Define what the agent must decide, what tools it needs, and what failure looks like.

Tool design

Design every tool the agent will call - typed inputs, deterministic outputs, error surfaces.

Orchestration

Wire together the reasoning loop, memory, routing logic, and sub-agent coordination.

Evaluation harness

Build a suite of golden cases. No agent ships without measurable pass rates.

Production hardening

Add retries, circuit breakers, cost limits, logging, and alerting before go-live.

Autonomy with accountability

Human-in-the-loop controls

Autonomy doesn't mean unaccountable. Every agent we build has explicit human oversight mechanisms.

Approval gates

High-stakes actions (sending emails, making payments, modifying records) pause and require explicit human sign-off before proceeding.

Confidence thresholds

Agents declare confidence scores. Below threshold, the task is routed to a human queue instead of auto-executing.

Audit logging

Every tool call, decision branch, and output is logged with timestamps and inputs. Replay any agent run for compliance review.

Rollback mechanisms

Actions are transactional where possible. Failed or flagged runs can be reversed without manual intervention.

Technology

Our agent stack

Orchestration

LangChainLLLlamaIndexCRCrewAIAUAutoGen

Models

GPTClaude SonnetGemini ProLlama

Memory

RedisPIPineconepgvectorMEMem0

Observability

LangSmithHEHeliconeCUCustom eval pipelinesOPOpenTelemetry

Measured, not guessed

Observability

You can't manage what you can't measure. Every agent ships with a full observability stack.

Agent run dashboard - live metrics

Token usage per run

avg 2,840 tokens / task

↓ 12% after prompt optimization

Tool call success rate

98.3% successful

↑ vs 91% at launch

Task completion rate

94.1% fully automated

5.9% escalated to human

Avg latency per step

1.2s per tool call

p95: 3.8s

Resource per run

0.041 avg

Target: <0.05

Escalation reasons

Top: low confidence (62%)

Ambiguous input (21%), API error (17%)

Agents in production

Legal tech

“Reduced contract review from 4 hours to 11 minuteswith a multi-agent legal analysis pipeline.”

94% of contracts now reviewed autonomously

Read case study

Customer operations

Agent handles 82% of tier-1 support tickets without human escalation.

82%

Auto-resolved

1.8s

Avg response

94%

CSAT score

60%

Efficiency gain

Read case study

FAQ

Common questions
about agent development.

Yes. We write custom tool adapters for internal databases, legacy REST services, and internal tooling. If your system has an endpoint, a CLI, or a webhook - we can make it a tool the agent can call.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Flagship Service

Agents that work.
Not agents that demo.

We design, build, and harden autonomous AI agents and multi-agent pipelines that operate reliably in production - not just in controlled environments.

Start a project See case studies

Reality check

What a real agent actually is

What people think

-A chatbot with a longer system prompt
-Automation that follows a fixed script
-A single LLM call with tools attached
-Works if the demo works

What production agents do

-Decompose goals into dynamic subtasks
-Recover from failures and retry with new strategies
-Coordinate across tools, APIs, and data sources
-Operate within defined guardrails autonomously

What makes ours different

-Evaluation harness before any deployment
-Observability from day one - every step logged
-Fallback paths for every critical action
-Hardened for edge cases, not just happy paths

What we build

Agent types we build

Workflow Agents

Automate multi-step business processes end-to-end with branching logic and approval gates.

Research Agents

Gather, synthesise, and deliver structured intelligence from the web, documents, and APIs.

Code Agents

Write, review, test, and deploy code autonomously - integrated into your CI/CD pipeline.

Data Agents

Query, clean, transform, and report on data in real time across warehouses and databases.

Customer-facing Agents

Handle support, onboarding, and sales conversations with context-aware reasoning.

How agents are wired together

Agent architecture patterns

The right architecture depends on task complexity, speed requirements, and how many steps are involved.

Simple tasks

Single agent

One LLM with a defined tool set handles a single, well-scoped task from start to finish.

User request

→ LLM reasons

→ Tool calls

→ Result

e.g. Document Q&A, form filling, data lookups

Multi-step workflows

Sequential pipeline

Agents are chained - each agent's output is the next agent's input. Deterministic ordering, easy to debug.

Input

→ Agent A (extract)

→ Agent B (classify)

→ Agent C (output)

e.g. Contract review → risk scoring → summary generation

Scale & speed

Parallel agents

Multiple agents run concurrently, coordinated by an orchestrator that merges results. 10× throughput.

Orchestrator

→ Agent 1 + Agent 2 + Agent 3 (parallel)

→ Merge

→ Output

e.g. Multi-source research, batch document processing

Industry applications

Use cases by industry

Healthcare

Clinical triage routing

Agent reads patient intake forms and routes cases to the right specialist queue - automatically.

Finance

Document extraction

Extracts, validates, and reconciles data across PDFs, spreadsheets, and banking APIs.

SaaS

Autonomous support

Resolves 80%+ of support tickets without human touch. Escalates when genuinely uncertain.

Logistics

Demand replanning

Monitors supply signals and replans demand forecasts in real time, surfacing actionable alerts.

E-commerce

Merchandising agent

Adjusts pricing, promotions, and product copy based on competitor signals and inventory levels.

Media

Content pipeline agent

Researches, drafts, formats, and schedules content across channels from a single brief.

What agents connect to

Tools & integrations

Agents are only as powerful as the tools they can use. Here's what we connect them to.

Web browsingCode executionREST APIsWebhooksSQL databasesVector storesEmailCalendarSlack / TeamsCRM (Salesforce, HubSpot)File systemsGitHubPDF / DOCX parsingSpreadsheetsZapier / Make

Need a custom tool? We write tool definitions and connection adapters for any API or internal system. If it has an endpoint, the agent can call it.

Our process

How we build agents

Problem scoping

Define what the agent must decide, what tools it needs, and what failure looks like.

Tool design

Design every tool the agent will call - typed inputs, deterministic outputs, error surfaces.

Orchestration

Wire together the reasoning loop, memory, routing logic, and sub-agent coordination.

Evaluation harness

Build a suite of golden cases. No agent ships without measurable pass rates.

Production hardening

Add retries, circuit breakers, cost limits, logging, and alerting before go-live.

Autonomy with accountability

Human-in-the-loop controls

Autonomy doesn't mean unaccountable. Every agent we build has explicit human oversight mechanisms.

Approval gates

High-stakes actions (sending emails, making payments, modifying records) pause and require explicit human sign-off before proceeding.

Confidence thresholds

Agents declare confidence scores. Below threshold, the task is routed to a human queue instead of auto-executing.

Audit logging

Every tool call, decision branch, and output is logged with timestamps and inputs. Replay any agent run for compliance review.

Rollback mechanisms

Actions are transactional where possible. Failed or flagged runs can be reversed without manual intervention.

Technology

Our agent stack

Orchestration

LangChainLLLlamaIndexCRCrewAIAUAutoGen

Models

GPTClaude SonnetGemini ProLlama

Memory

RedisPIPineconepgvectorMEMem0

Observability

LangSmithHEHeliconeCUCustom eval pipelinesOPOpenTelemetry

Measured, not guessed

Observability

You can't manage what you can't measure. Every agent ships with a full observability stack.

Agent run dashboard - live metrics

Token usage per run

avg 2,840 tokens / task

↓ 12% after prompt optimization

Tool call success rate

98.3% successful

↑ vs 91% at launch

Task completion rate

94.1% fully automated

5.9% escalated to human

Avg latency per step

1.2s per tool call

p95: 3.8s

Resource per run

0.041 avg

Target: <0.05

Escalation reasons

Top: low confidence (62%)

Ambiguous input (21%), API error (17%)

Agents in production

Legal tech

“Reduced contract review from 4 hours to 11 minuteswith a multi-agent legal analysis pipeline.”

94% of contracts now reviewed autonomously

Read case study

Customer operations

Agent handles 82% of tier-1 support tickets without human escalation.

82%

Auto-resolved

1.8s

Avg response

94%

CSAT score

60%

Efficiency gain

Read case study

FAQ

Common questions
about agent development.

Yes. We write custom tool adapters for internal databases, legacy REST services, and internal tooling. If your system has an endpoint, a CLI, or a webhook - we can make it a tool the agent can call.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Agents that work.Not agents that demo.

What a real agent actually is

Agent types we build

Agent architecture patterns

Use cases by industry

Tools & integrations

How we build agents

Human-in-the-loop controls

Our agent stack

Observability

Agents in production

Common questionsabout agent development.

Ready to build yournext digital product?

Tell us aboutyour project

Agents that work.Not agents that demo.

What a real agent actually is

Agent types we build

Agent architecture patterns

Use cases by industry

Tools & integrations

How we build agents

Human-in-the-loop controls

Our agent stack

Observability

Agents in production

Common questionsabout agent development.

Ready to build yournext digital product?

Tell us aboutyour project

Agents that work.Not agents that demo.

What a real agent actually is

Agent types we build

Agent architecture patterns

Use cases by industry

Tools & integrations

How we build agents

Human-in-the-loop controls

Our agent stack

Observability

Agents in production

Common questionsabout agent development.

Ready to build yournext digital product?

Tell us aboutyour project

Agents that work.
Not agents that demo.

Common questions
about agent development.

Ready to build your
next digital product?

Tell us about
your project

Agents that work.
Not agents that demo.

Common questions
about agent development.

Ready to build your
next digital product?

Tell us about
your project

Agents that work.
Not agents that demo.

Common questions
about agent development.

Ready to build your
next digital product?

Tell us about
your project