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
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.
Trusted by teams worldwide
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.
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.
The tickets, invoices and enquiries that pile up overnight get worked before anyone logs in. Your team arrives to exceptions rather than a backlog.
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.
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.
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.
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.
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.
Typed, validated calls into your CRM, ERP, helpdesk and internal APIs, with retries and rollback when a call does not land.
Answers built from your own documents and records with citations attached, so the agent cannot invent a policy that does not exist.
Approval gates on anything that spends money, contacts a customer or cannot be undone. You choose where the line sits.
Step limits, token budgets, allow-listed tools and PII redaction, enforced in code rather than requested in a prompt.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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.
Purchase orders, supplier chasing and quality paperwork worked without a coordinator.
Clinical and administrative load handled under HIPAA, with a person on anything clinical.
Order issues, returns and product questions resolved before a human sees them.
Document backlogs and exception handling across freight that never stops moving.
Billing queries, meter disputes and field scheduling worked end to end.
Claims files, photographs and policy wording read together rather than in three queues.
Student support and marking handled without diluting teaching time.
Underwriting, fraud and compliance work where the audit trail is not negotiable.
Leases, listings and tenant enquiries read at portfolio scale.
Delivered since 2018 across AI, mobile, cloud and enterprise builds.
Typical time from signed scope to a working agent on your own data.
One use case, one integration, quoted as a single number before we start.
Client operations running on Appther-built systems worldwide.
Agentic and AI builds we can point at: a voice agent handling live patient calls, a WhatsApp assistant closing support tickets, and route optimisation deciding against live constraints.

GPS units, RFID bin scanners and Bluetooth weigh scales feeding one offline-first driver app and live monitoring console.

An agent resolving support conversations end to end inside WhatsApp, so every weighment posts against the farmer slip automatically.

Live constraints from a 120-vehicle operation feeding an AI routing engine that decides and dispatches without a planner.
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.
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.
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.
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.
Prompts, orchestration code, evaluation sets and runbooks are yours, running in your own cloud accounts. An NDA is signed before discovery starts.
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.
An agent is only as safe as its loosest tool. Identity, permissions and action limits are treated as one system, not three separate features.
The controls below describe how we build and review code. They are engineering practices we apply on every project, not certifications we hold.
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.
Agent interfaces fail review for predictable reasons. We build against the rules that trip up AI features specifically.
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.
The building blocks that show up in most conngentic systems we ship, from first tool call through to estate-wide analytics.
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.
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.
01Temperature, vibration, GPS, current, image. Selection and calibration decide the quality of everything downstream, because no cloud layer recovers a bad reading.
02Runs 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.
03Breaks 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.
04Typed, allow-listed calls into your CRM, ERP, helpdesk and internal APIs, with validation, retries and rollback. Runs on AWS, Azure or Google Cloud.
05Short-term scratchpad, durable run state and long-term recall in a vector store, alongside a relational store for the business records that describe it.
06Spend caps, step limits, PII redaction and refusal handling enforced around the model, so your own systems and developers can build against the agent.
07Pairing, 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.
08Run health, failure thresholds and role-based access for the operations team who run the deployment day to day.
09Prediction, anomaly detection and computer vision applied to the live stream, trained on your own operating history rather than a generic benchmark.
10The 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.
01Temperature, vibration, GPS, current, image. Selection and calibration decide the quality of everything downstream, because no cloud layer recovers a bad reading.
02Runs 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.
03Breaks 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.
04Typed, allow-listed calls into your CRM, ERP, helpdesk and internal APIs, with validation, retries and rollback. Runs on AWS, Azure or Google Cloud.
05Short-term scratchpad, durable run state and long-term recall in a vector store, alongside a relational store for the business records that describe it.
06Spend caps, step limits, PII redaction and refusal handling enforced around the model, so your own systems and developers can build against the agent.
07Pairing, 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.
08Run health, failure thresholds and role-based access for the operations team who run the deployment day to day.
09Prediction, anomaly detection and computer vision applied to the live stream, trained on your own operating history rather than a generic benchmark.
10Protocols we build on
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.
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.
Which task, whose queue, and what "working" has to mean before anyone writes a prompt.ud decision is locked.
What the agent has to be worth before it is worth building.
Writing down the steps a person takes today, including the ones nobody documented.st.
The systems the agent has to reach, and the credentials to reach them safely.
Choosing the stack against your real tasks, then proving it on held-out cases.
The runtime that plans, calls tools, retries and keeps state across a task.
The contract between the agent and the systems you already run.
Where people meet the agent, and how it hands work back to a human.
Where your team watches what the agent did, and steps in when needed.
The limits that keep it safe, and the scoring that proves it works.
Real traffic, real outputs, no live consequences. The honest rehearsal.
Live on a slice of traffic first, so a bad release never reaches everyone.et.
The months after launch, where cost, accuracy and drift actually get managed.
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.
Prove it works on one workflow before committing further budget.
For teams past the experiment, putting agents on real volume.
Several agents across departments, with governance your auditors accept.
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.
“Appther built our student competition app exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”

“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.”

“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”

“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.”

Field notes from real agent builds. No hype, just what worked and what did not.
What an assistant actually costs to build and to run each month.
The stack behind a voice agent that holds a real conversation.
What changes when software starts finishing tasks rather than answering.
What to look for, and the questions worth asking before you hire.
Walk away from one call with the right protocol, a hardware direction, a realistic budget range and a delivery timeline. No pitch deck, no obligation.
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