AI Agent Development Company

Agents that finish work, not demos that answer questions. We build agentic systems that plan a task, call your tools and close it out, with a person approving anything that costs money or leaves the building. Fixed-price pilots from $4,900, live in 3 weeks.

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From First Pilot to Production: Our Complete Suite of AI Agent Development Services

Six agent types that cover where the work actually piles up. Each is scoped as a pilot first, so you see it working on your own data before anyone signs off on a platform.

Agent Types

AI support agent resolving a customer request end to end
01 · Agent type

Customer Support Agents

  • Resolve tickets, not just deflect them
  • Grounded in your own help centre and policies
  • Order lookups, refunds and returns via your APIs
  • Escalates to a human with full context attached
  • Every action written to an audit log
Explore Support Agents
Sales development agent qualifying inbound leads
02 · Agent type

Sales & SDR Agents

  • Qualify and route inbound within minutes
  • Research an account before the first reply
  • Draft follow-ups in your team’s voice
  • Write straight into Salesforce or HubSpot
  • Hands over the moment intent is real
Explore Sales Agents
Finance agent reconciling invoices against purchase orders
03 · Agent type

Back-Office & Finance Agents

  • Invoice capture, coding and three-way match
  • Exception queues instead of full manual review
  • Reconciliation across ERP and bank feeds
  • Approval thresholds you set, enforced in code
  • Reads the PDF nobody wants to open
Explore Finance Agents
Research agent assembling a sourced brief from internal data
04 · Agent type

Research & Reporting Agents

  • Multi-step research with citations
  • Competitor, market and account briefs
  • Recurring reports assembled and delivered
  • Pulls from your warehouse, not just the web
  • Says when the evidence is thin
Explore Research Agents
Operations agent triaging and routing work across systems
05 · Agent type

Operations & Workflow Agents

  • Triage, routing and status chasing
  • Cross-system updates without swivel-chairing
  • Scheduling and dispatch against real constraints
  • Retries and rollback when a tool call fails
  • Runs on a schedule or on an event
Explore Ops Agents
Orchestrated multi-agent system dividing a task between specialists
06 · Agent type

Multi-Agent Systems

  • A planner delegating to specialist agents
  • Shared memory and state across the run
  • Budget and step caps per task
  • Deterministic handoffs, not a free-for-all
  • Built on LangGraph or CrewAI
Explore Orchestration
Business Case

What Do You Actually Get From an AI Agent?

A chatbot answers. An agent finishes. The difference shows up as work that leaves your team's queue permanently, so here is where that pays for itself.

Queues that stop growing

The tickets, invoices and enquiries that pile up overnight get worked before anyone logs in. Your team arrives to exceptions rather than a backlog.

  • Tickets triaged and resolved unattended
  • Overnight queues cleared by morning
  • Humans see exceptions, not everything

Answers in seconds, at any hour

First response stops depending on who is awake. An agent grounded in your own policies replies immediately and correctly, or hands over with the context already gathered.

  • First response measured in seconds
  • Answers grounded in your own content
  • Clean handover when it should not decide

Capacity that does not need hiring

Volume doubles and the agent absorbs it. The cost curve is per task rather than per head, which is the whole reason to do this instead of recruiting.

  • Cost per task, not per hire
  • Absorbs peaks without a scramble
  • Same quality at 10 or 10,000 runs

Work you can actually audit

Every plan, tool call and decision is logged. When someone asks why the agent did that, there is an answer rather than a shrug at a black box.

  • Full trace of every action taken
  • Spend caps and step limits per task
  • Approval gates on anything irreversible
Engineer reviewing an agent run trace on a workstation

Agents That Actually Close the Loop

A demo that answers questions is not an agent. Six engineering disciplines separate the two, backed by our wider AI development and cloud teams. Want a conversational assistant instead of an autonomous one? See AI chatbot development.

[1] Planning & Task Decomposition

The agent breaks a goal into ordered steps, picks the tool for each and re-plans when one fails, instead of guessing at a single shot.

[2] Tool & API Calling

Typed, validated calls into your CRM, ERP, helpdesk and internal APIs, with retries and rollback when a call does not land.

[3] Grounded Retrieval

Answers built from your own documents and records with citations attached, so the agent cannot invent a policy that does not exist.

[4] Human-in-the-Loop Control

Approval gates on anything that spends money, contacts a customer or cannot be undone. You choose where the line sits.

[5] Guardrails & Spend Caps

Step limits, token budgets, allow-listed tools and PII redaction, enforced in code rather than requested in a prompt.

[6] Evaluation & Run Tracing

A held-out task set scored on every change, plus a full trace of each run, so quality is a number rather than an impression.

Technologies

The Agent Stack We Build On for Production Systems

What separates an agent that survives real traffic from one that impresses in a demo. Pick one to see where it earns its place in a build.

Agent Frameworks

LangGraph, CrewAI and the OpenAI and Anthropic SDKs, chosen for how well they express your control flow rather than by popularity. Deterministic handoffs and explicit state beat a clever prompt every time.

Model Routing

GPT, Claude, Gemini, Llama and Mistral, picked per step rather than per project. Planning gets the strong model, extraction gets the cheap one, and we show you the cost difference on your own traffic.

Guardrails & Policy

Allow-listed tools, typed arguments, step and token caps, PII redaction and refusal handling. Enforced in code around the model rather than requested politely inside the prompt.

Memory & State

Short-term scratchpads, durable run state and long-term recall in pgvector, Pinecone or OpenSearch, so an agent picks a task back up instead of starting from nothing.

Evaluation Harness

A held-out set of real tasks scored automatically on every prompt, model or tool change. Regressions surface in CI rather than in a customer conversation.

Observability & Tracing

Every plan, tool call, retry and token counted and traceable. When an agent does something odd you can replay the run instead of theorising about it.

Private Deployment

Open-weight models inside your own VPC or on-prem where regulated data cannot leave the tenancy. The usual answer in healthcare and finance, and increasingly under a strict DPA.

Not Sure Which Task to Automate? Get a Free Agent Audit.

One call about your workflows, your tools and where the hours go tells you which task is worth an agent first, what it will cost to run per month, and where a plain script would beat one. So your first dollar buys a result rather than a demo.

Get Your Free Audit

Free · No obligation · Delivered within 48 hours

Consultant and client agreeing the scope of an AI agent pilot
Industries

AI Agents Across Every Industry

Nine sectors where agents are already taking work off a queue. See our healthcare and fintech practices for deeper detail, or enterprise software when the agent has to reach the rest of the business.

[01] Manufacturing

Purchase orders, supplier chasing and quality paperwork worked without a coordinator.

  • Supplier and PO chasing
  • Non-conformance triage
  • Spec and drawing lookup
Know more about Manufacturing
[02] Healthcare

Clinical and administrative load handled under HIPAA, with a person on anything clinical.

  • Prior authorisation packs
  • Intake and scheduling agents
  • Clinical note summarisation
Know more about Healthcare
[03] Retail & E-Commerce

Order issues, returns and product questions resolved before a human sees them.

  • Order status and returns
  • Product enrichment at scale
  • Review triage and response
Know more about Retail
[04] Logistics & Supply Chain

Document backlogs and exception handling across freight that never stops moving.

  • Customs and BOL extraction
  • Exception routing and chasing
  • ETA queries answered instantly
Know more about Logistics
[05] Energy & Utilities

Billing queries, meter disputes and field scheduling worked end to end.

  • Billing and meter disputes
  • Outage comms drafted and sent
  • Field visit scheduling
Know more about Enterprise
[06] Insurance & Claims

Claims files, photographs and policy wording read together rather than in three queues.

  • First-notice-of-loss triage
  • Damage assessment from photos
  • Policy wording comparison
Know more about Insurance
[07] Education & EdTech

Student support and marking handled without diluting teaching time.

  • Always-on student assistants
  • Assisted marking with rubrics
  • Enrolment and admin queries
Know more about Education
[08] FinTech & Payments

Underwriting, fraud and compliance work where the audit trail is not negotiable.

  • KYC and document verification
  • Chargeback and dispute packs
  • Fraud review with explanations
Know more about FinTech
[09] Real Estate & PropTech

Leases, listings and tenant enquiries read at portfolio scale.

  • Lease abstraction and review
  • Tenant enquiry agents
  • Valuation support packs
Know more about Real Estate
Measurable Impact

Numbers From Agents in Production

200+

Products shipped

Delivered since 2018 across AI, mobile, cloud and enterprise builds.

3 wks

Pilot to live

Typical time from signed scope to a working agent on your own data.

$4,900

Fixed-price pilot

One use case, one integration, quoted as a single number before we start.

15+

Countries served

Client operations running on Appther-built systems worldwide.

Gain a Competitive Edge

Why Partner With Appther for AI Agent Development?

Plenty of agencies can wire up a demo. Fewer will run it against your real data in week one and tell you plainly where it fails. As an AI agent development company we cover the whole connected stack in house, alongside our AI development and cloud infrastructure teams.

01

Scoped as a pilot, not a programme

One use case, one integration, a fixed price and three weeks. You see it working on your own data before anyone signs off on a platform, and the integration work is quoted the same way. No three-vendor standoff when a task fails between the model and your database.

02

Evaluated, not demoed

A held-out set of your real tasks, scored on every change. We report the failure rate rather than showing you the runs that went well.

03

Guardrails written in code

Spend caps, step limits, allow-listed tools and approval gates enforced around the model, not requested inside the prompt. Where it earns its keep we build digital twins on the live stream, backed by our AI engineering practice.

04

You own the whole thing

Prompts, orchestration code, evaluation sets and runbooks are yours, running in your own cloud accounts. An NDA is signed before discovery starts.

Security & Compliance

How Does Appther Keep an Agent Safe?

An agent that can act is an agent that can act wrongly. Here is how we constrain that, and only what we can honestly claim.

Prompts and traces carry personal data more often than teams expect. We design around minimisation first, so compliance is a property of the architecture rather than a later retrofit.

GDPRLawful basis mapped per data type, EU residency options, and working export and deletion endpoints built in from the first sprint.
CCPA / CPRAConsumer disclosure, opt-out handling and deletion that reaches prompts, traces and any fine-tuned weights.
India DPDP ActConsent capture and notice flows for deployments serving Indian users, with purpose limitation on stored runs.
Data minimisationWe pass the model the fields the task needs and redact the rest before the call, which cuts both risk and token cost.

An agent is only as safe as its loosest tool. Identity, permissions and action limits are treated as one system, not three separate features.

Per-agent identityScoped service accounts per agent so one can be revoked without touching the rest of the estate.
Encrypted transportTLS for every model and tool call, AES-256 at rest, and PII redacted before anything leaves your tenancy.
Allow-listed toolsAn agent can call only the tools it was granted, with typed arguments validated before execution and rollback on failure.
Network segmentationAgent traffic isolated from business systems, with least-privilege rules between orchestration, storage and tool tiers.

The controls below describe how we build and review code. They are engineering practices we apply on every project, not certifications we hold.

OWASP Mobile Top 10Reviewed against every mobile release, with secrets kept out of the app binary as a standing rule.
ISO/IEC 27001 alignedAccess control, change management and audit logging modelled on the standard's technical controls.
Secure coding & reviewPeer review on every merge, dependency scanning in CI, and secrets management outside the repository.
Penetration testingThird-party testing arranged before launch on request, with findings tracked to closure.

To be explicit: Appther is not a certification body. Formal ISO, SOC 2 or HIPAA attestation of your finished product sits with your compliance team and its auditor. We build and document so that audit is straightforward.

Regulated sectors change the architecture, not just the paperwork. We scope these constraints during discovery so they never surface late.

HIPAA-ready buildsFor health workloads we architect to the technical safeguards: access control, audit logs and BAA-compatible model hosting.
PCI DSS scope reductionAgents built so card data never reaches a prompt, staying inside the payment provider and out of the model context.
Industrial safety systemsRead-only tool access by default, so an agent that can look at a system cannot change it without an explicit grant.
Model & data residencyWe flag where a model provider processes data early, and can host open weights in your own region instead.

Agent interfaces fail review for predictable reasons. We build against the rules that trip up AI features specifically.

Apple App Store reviewAI disclosure, data-use strings and account-deletion paths prepared to survive first submission.
Google Play policyGenerative-AI policy disclosures handled correctly, with a data safety form that matches what the agent actually sends.
WCAG 2.2 AAContrast, focus order and screen-reader labelling checked on the app, not just the marketing site.
Offline & failure statesEvery refusal, timeout and handover gets a message a real user can act on, which is also what reviewers look for.
Tech Stack

Protocols, Platforms & Frameworks

The agent stack we build on, from orchestration and tool calling through to vector and relational storage and performance work once the agent is live.

From the radio on the board to the dashboard on the wall, grouped by the layer it belongs to.

  • LangGraph
  • CrewAI
  • OpenAI SDK
  • Anthropic SDK
  • LlamaIndex
  • LangChain
  • pgvector
  • Pinecone
  • Qdrant
  • LangGraph
  • CrewAI
  • OpenAI SDK
  • Anthropic SDK
  • LlamaIndex
  • LangChain
  • pgvector
  • Pinecone
  • Qdrant
  • ESP32
  • Arduino
  • Raspberry Pi
  • STM32
  • Nordic nRF
  • FreeRTOS
  • Zephyr
  • C / C++
  • Embedded Linux
  • ESP32
  • Arduino
  • Raspberry Pi
  • STM32
  • Nordic nRF
  • FreeRTOS
  • Zephyr
  • C / C++
  • Embedded Linux
  • AWS Bedrock
  • Azure OpenAI
  • Google Vertex AI
  • Node.js
  • Python
  • Docker
  • Kubernetes
  • Terraform
  • PostgreSQL
  • AWS Bedrock
  • Azure OpenAI
  • Google Vertex AI
  • Node.js
  • Python
  • Docker
  • Kubernetes
  • Terraform
  • PostgreSQL
  • Swift
  • Kotlin
  • React Native
  • Flutter
  • InfluxDB
  • TimescaleDB
  • Grafana
  • TensorFlow Lite
  • Swift
  • Kotlin
  • React Native
  • Flutter
  • InfluxDB
  • TimescaleDB
  • Grafana
  • TensorFlow Lite
Feature Set

Key Features We Deliver in Agent Builds

The building blocks that show up in most conngentic systems we ship, from first tool call through to estate-wide analytics.

  • Tool & API Integration
  • Multi-Step Task Planning
  • Retry & Rollback on Failure
  • Streaming Responses
  • Grounded Retrieval with Citations
  • Run Trace & Audit Dashboard
  • PII Redaction in Transit
  • Scoped Credentials per Agent
  • Multi-Channel (Chat, Email, Voice, WhatsApp)
  • ERP, CRM & Helpdesk Integrations
  • Role-Based Access Control
  • Escalation & Handover Rules
  • Agent Versioning & Rollout
  • Document & Vision Understanding
  • Cost & Latency Analytics
  • Prompt & Model Cost Tuning
Architecture

Agent Architecture, Layer by Layer

An agent is ten layers that have to agree with each other. Get one wrong and the symptom usually shows up somewhere else, which is why we design them together rather than in sequence.

Channels

The endpoint itself: the chat, email, voice or WhatsApp surface a person actually reaches. Chosen against where your customers already are and how long it has to survive in the field.

01

Intent & Routing

Temperature, vibration, GPS, current, image. Selection and calibration decide the quality of everything downstream, because no cloud layer recovers a bad reading.

02

Orchestrator

Runs the loop: chooses the next step, dispatches it, handles the result and decides whether to continue. Deterministic control flow rather than a single open-ended prompt. The gateway is what translates them onto IP.

03

Planner

Breaks the goal into ordered steps and re-plans when one fails. Keeps a step budget so a stuck task ends rather than burning tokens. This is where costidth, extends battery life and keeps the system working when the link drops.

04

Tool Layer

Typed, allow-listed calls into your CRM, ERP, helpdesk and internal APIs, with validation, retries and rollback. Runs on AWS, Azure or Google Cloud.

05

Memory & Vector Store

Short-term scratchpad, durable run state and long-term recall in a vector store, alongside a relational store for the business records that describe it.

06

Guardrails

Spend caps, step limits, PII redaction and refusal handling enforced around the model, so your own systems and developers can build against the agent.

07

Human Review

Pairing, provisioning and control in the user’s hand. Offline-first, because a connected product that needs a connection to set up will fail at the doorstep.

08

Trace & Eval

Run health, failure thresholds and role-based access for the operations team who run the deployment day to day.

09

Model Layer

Prediction, anomaly detection and computer vision applied to the live stream, trained on your own operating history rather than a generic benchmark.

10

Channels

The endpoint itself: the chat, email, voice or WhatsApp surface a person actually reaches. Chosen against where your customers already are and how long it has to survive in the field.

01

Intent & Routing

Temperature, vibration, GPS, current, image. Selection and calibration decide the quality of everything downstream, because no cloud layer recovers a bad reading.

02

Orchestrator

Runs the loop: chooses the next step, dispatches it, handles the result and decides whether to continue. Deterministic control flow rather than a single open-ended prompt. The gateway is what translates them onto IP.

03

Planner

Breaks the goal into ordered steps and re-plans when one fails. Keeps a step budget so a stuck task ends rather than burning tokens. This is where costidth, extends battery life and keeps the system working when the link drops.

04

Tool Layer

Typed, allow-listed calls into your CRM, ERP, helpdesk and internal APIs, with validation, retries and rollback. Runs on AWS, Azure or Google Cloud.

05

Memory & Vector Store

Short-term scratchpad, durable run state and long-term recall in a vector store, alongside a relational store for the business records that describe it.

06

Guardrails

Spend caps, step limits, PII redaction and refusal handling enforced around the model, so your own systems and developers can build against the agent.

07

Human Review

Pairing, provisioning and control in the user’s hand. Offline-first, because a connected product that needs a connection to set up will fail at the doorstep.

08

Trace & Eval

Run health, failure thresholds and role-based access for the operations team who run the deployment day to day.

09

Model Layer

Prediction, anomaly detection and computer vision applied to the live stream, trained on your own operating history rather than a generic benchmark.

10

Protocols we build on

LangGraphCrewAIpgvectorOpenAIAnthropicBedrockWebSocketsRedis

Which one fits is decided by range, power budget and data volume, not preference. See our hardware app development and AI development services for the layers either side of the model.

Our Process

End-to-End Agent Development Process

Thirteen stages take an agent from first conversation to live traffic. Autonomy punishes guesswork, so the risky parts get proven early and you see a working build at the end of every sprint.

Week 1

Discovery

Which task, whose queue, and what "working" has to mean before anyone writes a prompt.ud decision is locked.

What we doMap the task end to end, the systems it touches and the regulatory obligations attached to it. Existing hardware and SDKs get reviewed here, not later.
What you getA written problem statement, the constraints that drive every later choice, and a shortlist of viable approaches.
Week 1–2

Business Case

What the agent has to be worth before it is worth building.

What we doModel the hours or cost the agent is meant to remove, size the task volume at launch and at scale, and set the metrics go-live will be judged on.
What you getA business case with the numbers behind it, and a scope you can hold us to.
Week 2–3

Task Mapping

Writing down the steps a person takes today, including the ones nobody documented.st.

What we doSit with the people doing the work, record the decisions they make and the exceptions they hit. ESP32, STM32, Nordic nRF and Raspberry Pi are the usual candidates.
What you getA documented task flow with the edge cases named, and the ones we will not automate yet.
Week 3–8

Tool & Data Access

The systems the agent has to reach, and the credentials to reach them safely.

What we doScoped service accounts, typed tool definitions, sandbox credentials and rate limits agreed with your team. Written in C/C++ on FreeRTOS or Zephyr, or embedded Linux where the workload needs it.
What you getA tool layer your security team has signed off, with least-privilege access per agent.
Week 3–6

Model & Framework Selection

Choosing the stack against your real tasks, then proving it on held-out cases.

What we doCompare frameworks, models and retrieval strategies against your real tasks, then stress the chosen path with real interference and real distance.
What you getA model and framework decision with the benchmark behind it, and a fallback when a call fails.
Week 5–12

Orchestration Build

The runtime that plans, calls tools, retries and keeps state across a task.

What we doStand up orchestration on AWS, Azure or Google Cloud, size the vector store, and set up per-agent identity, provisioning and staged rollout.
What you getInfrastructure as code, a documented run model, and a cost projection at ten times launch volume.
Week 7–13

Integration Layer

The contract between the agent and the systems you already run.

What we doBuild typed, validated tool endpoints with authentication, rate limiting and versioning. Where it has to reach further, we connect to your ERP, CRM or SCADA.
What you getDocumented, versioned tool contracts your own developers can extend without asking us.
Week 9–16

Channels & Handover

Where people meet the agent, and how it hands work back to a human.

What we doChat, email, voice or WhatsApp, wired to the inbox your customers already use. Escalation rules, offline-first sync and push alerts get the most attention, because that is where connected apps usually fail.
What you getA working channel with a clean handover path, tested with your own team first.
Week 10–17

Trace & Ops Console

Where your team watches what the agent did, and steps in when needed.

What we doRun traces, cost and latency, failure rates and role-based access, backed by storage built for high-frequency data.
What you getAn operations console with the views your team asked for, and exports for the ones they have not thought of yet.
Week 12–19

Guardrails & Evaluation

The limits that keep it safe, and the scoring that proves it works.

What we doSpend caps, step limits, allow-listed tools and PII redaction, plus a held-out task set scored automatically where the link cannot be relied on. Grounded in your own operating history.
What you getGuardrails you can point at in an audit, and a quality number tracked after release rather than assumed.
Week 14–21

Shadow Running

Real traffic, real outputs, no live consequences. The honest rehearsal.

What we doThe agent runs against live traffic with its actions logged but not executed, plus load testing the cloud tier and a security review of the device identity chain.
What you getA measured failure rate, a security review and a supervised pilot before anything goes live.
Week 20–22

Phased Rollout

Live on a slice of traffic first, so a bad release never reaches everyone.et.

What we doLive on a traffic slice first, widened as the numbers hold, with instant rollback per agent version.
What you getA live agent, monitoring dashboards and a rollback path that has been tested.
Ongoing

Support & Tuning

The months after launch, where cost, accuracy and drift actually get managed.

What we doPrompt and model updates, credential rotation, cost tuning as volume grows, and an on-call path for incidents.
What you getA support agreement sized to your volume, and the same engineers who built it.
Pricing

Fixed Price, Agreed Before We Start

Round numbers so you can budget today. The exact figure is confirmed in writing after a free scoping call, and it does not move unless you change the scope.

Agent Pilot

Prove it works on one workflow before committing further budget.

$4,900
Live in 2 to 3 weeks
  • One agent, one workflow
  • Up to 3 tool integrations
  • Guardrails and approval gates
  • Shadow-mode validation
  • 30 days post-launch tuning
Start a Pilot

Agent Platform

Several agents across departments, with governance your auditors accept.

$15,900 to $19,900
Phased over 8 to 10 weeks
  • Multi-agent orchestration
  • Central governance and audit console
  • Private or in-VPC model deployment
  • SSO, RBAC and data residency controls
  • SLA-backed support and enablement
Talk to an Architect

All tiers include source code, prompts and evaluation sets. You own everything, with no proprietary runtime and no lock-in. Model and infrastructure usage is billed at cost, typically $50 to $600 a month depending on volume. Not sure which tier fits? Take the free audit and we will tell you.

Client Voices

Trusted By Teams Who Ship

“Appther built our student competition app exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”
Dionna Milem
Dionna MilemOwner · My BOB Team LLC
“Building a mental health app means every detail has to be handled with care, and Appther got that from day one. The product feels warm, simple, and genuinely helpful.”
Bob Singhal
Bob SinghalProfessor of Joy · Joyscore Inc
“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”
Johnathan Peffer
Johnathan PefferCEO · Bull's Eye Technologies Inc
“Implementing an ERP across our operations felt daunting at first, but Appther made the transition seamless. Every department now works off a single source of truth.”
Pushpender Kumar
Pushpender KumarGeneral Manager · AB Sugar Ltd
Latest Insights

Notes on Building AI Agents

Field notes from real agent builds. No hype, just what worked and what did not.

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FAQ

Questions We Hear Every Week

A chatbot answers questions. An AI agent completes tasks. It can plan a sequence of steps, call your systems through tools and APIs, check its own work, and decide when to escalate to a person. A support chatbot tells a customer your refund policy; a support agent issues the refund, updates the order and logs the ticket.
A single-workflow pilot is $4,900 and goes live in 2 to 3 weeks. Production builds covering two to three agents with real system integrations run $8,900 to $14,900. Multi-agent platforms with orchestration and governance run $15,900 to $19,900. You get a fixed-price quote after a free scoping call, so there is no open-ended billing.
Two to three weeks for a pilot on one workflow, and 4 to 6 weeks for a production build across several workflows with integrations. We scope to a fixed date in week one and run in weekly sprints, so you see a working agent well before launch rather than at the end.
Yes. Agents act through tools we build against your stack: CRM, helpdesk, ERP, billing, data warehouse and internal APIs. We have integrated agents with Odoo, Salesforce, HubSpot, Zendesk, Xero, QuickBooks, NetSuite and custom systems. If it has an API or a database, an agent can use it.
Every agent runs inside limits set before launch: which tools it can call, what it can change, and the value thresholds above which a human must approve. High-risk actions such as refunds, payments and data deletion sit behind approval by default. Every action is logged with its reasoning, so you can audit exactly what happened and why.
It hands off to a person with the full context attached, rather than looping or guessing. Handoff rules are part of the design, not an afterthought, and we tune them from real conversations after launch. Most teams settle around 60 to 75 percent full automation, with the rest routed to humans faster and better prepared than before.
No. Discovery includes an honest look at your data and systems, and we tell you upfront if something needs fixing first. In most cases we start with the workflow where your data is already good enough, prove the value there, then expand. That is faster and cheaper than a data project with no visible outcome.
You do. You get the full source, the prompts, the evaluation sets and the deployment configuration. There is no proprietary runtime you have to keep paying us for, and no lock-in. An NDA is available before any discovery conversation.
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