AI Agent Development Services for software that decides the next step, then takes it.
We build AI agents for product and operations teams to carry out one job across agreed systems. Our AI agent development defines the goal, actions and approval boundaries, then tests performance on representative tasks.
- 120+Projects delivered across 12 countries since 2012
- 100+Senior in-house engineers, six countries
- 5.0Unified rating across 61 client reviews on Clutch
- ISO 27001Certified security practice, audited by Quay Audit UK
- AWS CertifiedSolutions Architects on the team
01Overview
Scope one job for an agent
We turn a defined workflow into an agent brief: the systems it can use, the actions it can take and what done means. Discovery establishes those requirements with the people responsible for the task.
- One agent, one jobWe identify the task, inputs and completion criteria with the people who do the work today. The brief gives the pilot a specific result to demonstrate.
- Approval boundaries you chooseWe agree which actions require human approval. Changes to that authority depend on evaluation evidence and the responsible owner's decision.
- Built to your requirementsWe build agents to your requirements, including permissions for each tool and the records the agent may read or update. Those requirements guide the implementation.
- The rest of the AI clusterChoose AI integration for model features in an existing product, conversational and voice AI for conversation processing, or MCP for a dedicated tool-interface build.
02Proof
Agent and AI engineering experience
Our experience spans an internal agent proof of concept, production search features and AI products we own and operate.
- A browser-based internal proof of conceptOur internal AI web agent used a text prompt to navigate unicrew's project management system, find missing time logs for the preceding week and notify the people and their managers through Google Chat. The proof of concept used Python, LangChain and AWS Bedrock to test this defined administrative job.30%improvement in timely work-time logging compliance on unicrew's own team
- AI inside a client's own productAt meinUnterricht we worked on relevance, filtering and autosuggest alongside AI tagging and embeddings. The client reported roughly 9% higher search success in a controlled experiment across that feature work. The features were live in production, and the tagging and embeddings prepared the move to vector search.~9%higher search success rate at meinUnterricht, measured in a controlled experiment
- Two owned, revenue-generating AI productsSnaplore is our knowledge-management AI product, using Whisper, GPT models and AssemblyAI. Talkmetry is our call-intelligence product for HubSpot, using GPT and AssemblyAI. Operating these products gives us experience with the model and infrastructure choices an AI feature depends on.2AI products of our own, revenue-generating; Snaplore has active users
03Compare
Agent, chatbot or script: which one fits?
Compare the task and control you need, including packaged agents available in your existing platforms. Your internal team can implement an approach when it can own the integration, evaluation and operation.
| An AI agentunicrew | Scripted automation or RPAAutomate | Chatbot or assistantAssist |
|---|---|---|
| Best forA defined job with varied steps, where software needs to choose actions using tool results and feedback. | Best forA workflow whose rules and branches can be specified, including predictable exceptions. | Best forInteractive assistance where a user directs or confirms the next step, including permitted tool calls. |
| What it asks of youRepresentative tasks, permitted actions, evals and guardrails, plus an operating owner and model-usage budget. | What it asks of youDefined rules, tests, access controls and maintenance as interfaces or requirements change. | What it asks of youApproved information and tools, evaluation of responses and a clear role for the user in completing the work. |
| You end up owningThe commissioned agent, evaluation and access deliverables, with handover and rights defined in the agreement. | You end up owningAn automated workflow and its agreed code or configuration, with a plan for maintaining the rules. | You end up owningAn assistant configured for the task, with agreed content, tool connections and handover. |
Quick self-check
Tick what is true for you. The read-out updates as you go.
0 of 4 true
Start with scripted automation
A workflow with defined rules and branches may fit scripted automation. Compare that approach with an agent against the variation in the task and the control you need.
Talk it throughDefine the task and its result
Describe the job, its inputs and what successful completion looks like. We can then discuss whether it needs action selection, a fixed workflow or user-directed assistance.
Talk it throughRun a pilot before you fund a rollout
A pilot can test the defined job on representative tasks in a contained environment. Agree the completion criteria and permitted tools before using the result to plan a rollout.
Book a discovery callThe shape fits; the open question is authority
For a job that needs decisions across steps, define the actions the agent may take and those requiring a person. That authority belongs in the brief and acceptance tests.
Book a discovery callAn agent, with the autonomy gate designed first
Where the job includes consequential actions, design the approval rules and evaluations alongside the tool connections. The pilot should test those boundaries as well as task completion.
Talk about the workOn an AI agent build, governance is the first thing we design. We agree which actions the agent takes alone, which need a human sign-off, and how its decisions are logged. We also define how it handles a case it was not built for.
Andrii BurdaSenior Engineering Manager, unicrew04Capabilities
What we build
We build the task logic, tool connections and approval paths needed for the job you commission.
- Workflow automation agents
Workflow agents that execute multi-step tasks
One job, a definition of doneWe build an agent that reads a request, plans the steps and takes actions through the permitted tools. The workflow returns a defined task result. Scripted automation remains an option where the rules and branches can be specified.
- Agents inside your own tools
Agents embedded in your existing systems
Inside your CRM or helpdeskWe connect the agent to your CRM, helpdesk or internal tools and implement the agreed read, update and approval paths. The product's existing interfaces and access controls shape those connections.
- Tool access for agents
The MCP access layer
Model Context ProtocolWe use MCP (Model Context Protocol) as a tool interface where it fits the integration. The connected systems still need authorization and access controls. Building and securing the servers is a distinct scope.
- Agent guardrails and autonomy limits
Evals and guardrails
Scored against real tasksWe evaluate task completion and failure modes on representative work, then test the agreed guardrails and approval boundaries. Evaluations belong in the pilot; standing evaluation and observability work can be commissioned separately.
- Human-in-the-loop agent design
Human-in-the-loop and access design
One action at a timeWe design which actions the agent may take and which require human approval, including proposals that need operator review before updating a record. Access and approval requirements become part of the task specification and tests.
- Conversational and voice agents
Voice and conversational agents
Speech, turn-taking, handoverWe connect conversational input to a defined action workflow, such as booking, triage or qualification. Speech, turn-taking and the information handed to a person need their own acceptance criteria.
- Internal operations automation
Internal-operations agents
Internal workflowsWe scope agents for timesheet follow-up, record reconciliation and ticket triage through approved tools. The brief names the task output, operating owner and actions needing review.
05Trust
The agent, evaluations and access specification
We deliver the agent and the evidence needed to assess its task performance and permitted authority. Code, evaluation and access handover follow the agreed scope, with third-party rights and dependencies identified.
- One jobAn agent with a written definition of doneThe boundary is the deliverable: what it owns, and what it hands back to a person
- Yours to rerunAn eval suite scored on your own tasksHanded over with the failure-mode list, so autonomy is earned rather than assumed
- ScopedAccess written down, per toolWhat the agent may read and what it may commit, under our ISO 27001:2022 certified practice
06Stack
The stack we build agents on
We select models, orchestration tools and runtime services against task quality, tool compatibility, deployment requirements and operating cost. AWS Bedrock, LangChain and Python supported our internal proof of concept.
Python- REST API
AWS Bedrock
LangChain
OpenAI / ChatGPT
07Engagement
Pilot, team or advisory
The first purchase follows the decision ahead: testing a defined job, delivering several agent workflows or selecting the use case to build.
A scoped pilot first
Start hereWe test one job on representative tasks in a contained setup, including evaluations and the agreed action boundaries. The result supports a decision on production hardening and rollout.
- Best when
- One job is already the obvious candidate and you want proof before a budget
- You pay
- Outcome based, quoted per project
- Typical start
- Two to four weeks
A dedicated team
Long-runExtend your engineering team for successive agent workflows and their system connections. Agree delivery, evaluation and operating responsibilities with your technical lead as the work develops.
- Best when
- More jobs are coming and one team should be answerable for all of them
- You pay
- Billed monthly, per team member
- Typical start
- Two to four weeks
Advisory before you build
Decision firstWe assess candidate jobs, data readiness and the authority each workflow would need. The recommendation helps you choose the first build and its acceptance requirements.
- Best when
- You have several candidate use cases and no agreed way to rank them
- You pay
- Billed hourly, quoted per project
- Typical start
- Two to four weeks
Score it before you decide whether to keep it
We assess an inherited agent against representative tasks, its permitted actions and operating requirements. The written recommendation identifies whether to promote, harden or stop the current approach. Commissioning changes is a separate decision. There is no minimum engagement period.
08Industries
Industries we build agents for
Start with the sector closest to the job you want an agent to complete.
Logistics and transportation
We define agent tasks for order, warehouse and fleet operations, including vehicle inventory and inspection workflows. The specification distinguishes record checks, proposed updates and actions requiring approval.
Hospitality and leisure
We scope agent actions within booking platforms and connected property management systems (PMS), separating availability lookups, proposed booking changes and approved commitments. Platform permissions and booking rules shape the workflow's approval path.
EdTech and learning
We design content-search tasks around relevant records and source material. Search, tagging and embeddings provide the data connections an agent can use, with task quality tested against the content users need.
Fintech and accounting
For bookkeeping and invoice workflows, we define permitted record access, proposed reconciliation actions and human approval. The task needs an acceptance standard for both the result and the action taken.
Healthcare
Within home-health software, we scope operating tasks against data permissions and review points approved by the domain owner. The brief defines the task result and which actions require a person.
E-commerce
Our developers worked on commerce middleware between Shopify storefronts and a Microsoft Dynamics ERP. For an agent build, we map stock and order actions to permitted tools and approval rules.
Bring one job to an agent discovery call
Describe the task, expected result and systems involved. We will discuss an agent, scripted workflow or assistant against those requirements and define the evidence a pilot should provide.
What happens after you contact us
- We reply within one business dayA response to your enquiry so we can establish the job and next step.
- A call about the one job, not the modelWhich job, what it would read, what it would be allowed to do without asking, and who signs off on the actions that carry consequences.
- A written agent briefThe single job, the systems it has to reach, the actions that need a person, and what done means.
- Contracts and NDAs, then a start dateWe agree scope, access and the start date. Most engagements start within two to four weeks.
09Delivery
From agent brief to operating workflow
An agent build moves from job scope and tool connections to a pilot, approval gates and operation. Your task and system owners provide the evidence and approve the authority needed at each stage.
- Scope the job the agent ownsWe define the job, inputs and completion criteria with the people who do the work today. You provide representative tasks and the owner who can approve the intended result.You getA written agent brief: the one job, the tools it may use, the actions that need a person, and what done means.
- Map and open the systems it has to reachWe identify the records and tool actions needed, then implement the approved connections. Your technical owner provides system documentation, permissions and an appropriate test environment.You getAn access map, and a working connection to each system with per-tool scopes written down.
- Pilot against real tasksWe run representative tasks in a contained setup and measure completion and failure modes. Your domain owner judges the results against the agreed acceptance criteria.You getA completion rate and a failure-mode list measured on your own work, plus a go or no-go recommendation.
- Gate with evals and guardrailsWe test evaluations, guardrails, permissions and approval routes against the task cases. The responsible owner approves any change to action authority using that evidence; autonomy does not increase automatically.You getAn eval suite you can rerun, a guardrail and approval policy, and a written autonomy level for every action.
- Run with monitoring and cost controlsWe configure the agreed monitoring, drift checks and cost controls. You name the operating owner and usage budget; together we define how results are reviewed and changes approved.You getMonitoring and drift checks, a token and infrastructure cost budget, and a named owner for changes.
10Client voices
Client views on AI, data and system work
Our headline result was a controlled experiment showing a roughly 9 percent lift in search success rate. The features built are live in production, and the AI tagging and embeddings work set up our move to vector search. What stands out most is their ability to own work end to end, from user-facing search features to the AI and data layer underneath.
Even though Artelogic didn’t have a background in this area, they learned quickly and repurposed technologies they’d used before in order to solve the business problem. I was very impressed with this ability, as most of the people we contacted before implied that they’d need to spend a lot of time of trying to understand our business logic.
Shippable and well received web GUI for previously API only application. The whole team is highly dedicated to the success of the project and delivers the planned artefacts on time. We are very happy to have chosen Artelogic.
Artelogic’s work had a very positive impact on our team’s morale. As our development quality was improving, our engineers were more confident in what they were doing, allowing them to work faster and with more confidence. As a result, our releases took less time and were less stressful.
They always seem to have a suitable developer on standby when we require an additional skillset and they always respond very quickly and help to work out a solution if e.g. the project requirements are suddenly changing.
11Case studies
Our internal agent and owned AI product
Our internal administrative agent ran as a proof of concept; Snaplore is an owned, revenue-generating AI product. unicrew has built software since 2012 and was formerly Artelogic, so the older reviews use that name.
See all case studies
Built by unicrewAI-Powered Automation for Project ManagementAn AI bot that enhances operational efficiency by automating a time-consuming internal administrative task.30%Improvement in timely work-time logging compliance
Built by unicrewAI-powered knowledge managementSnaplore is unicrew's own product, built and operated in-house. It uses AI to transform how organizations document, structure, and share information, making meetings, training, and project discussions instantly accessible and actionable.Up to 60%Less time on documentation
12Questions
AI agent development: scope and operation
An agent's task performance and permitted actions need separate acceptance criteria. Agree how both will be assessed before commissioning the build.
An AI agent plans the steps toward a goal with a model and takes actions through tools, using results and feedback to decide what follows. Scripted automation follows rules and branches specified in advance.
Scripts can handle exceptions and be resilient; both approaches need testing and access controls. An agent needs evaluations and guardrails for its variable behavior. Agentic AI describes this goal-directed, tool-using approach and can involve one agent or several. A chatbot can also use tools while keeping the user in control of the next step.
We quote AI agent development against the agreed job and implementation scope, with running costs estimated separately.
Build costs depend on:
- Data preparation and system or tool connections.
- The permitted actions and their approval requirements.
- Evaluation, guardrail and deployment work.
Operation adds model usage, infrastructure and the work agreed for monitoring and changes. Billing follows the engagement model:
- Time and materials: billed hourly, quoted per project.
- Fixed price: outcome based, quoted per project.
- A dedicated team: billed monthly per team member.
We estimate pilot and production schedules after defining the task, tool access, data preparation and acceptance requirements. The pilot needs representative work and an owner who can judge the result; production adds the agreed deployment and operating scope.
Most engagements start within two to four weeks. That is a qualified start window, separate from the delivery schedule. Our guide to AI agents for business explains the deployment considerations; your project plan sets the milestones for your build.
Yes, where the systems provide interfaces and permissions suitable for the task. We assess those connections and the actions the agent needs before implementing them.
APIs and MCP tool interfaces are options; authorization remains the responsibility of the connected applications. Our internal proof of concept used a browser workflow, another possible connection where access permits it. Browser-based integrations need maintenance when the interface changes. The scope includes those dependencies and the owner who approves access.
Production operation needs tested task performance, enforced access and approval rules, and a responsible operating owner.
The design should include:
- Evaluations on representative tasks and failure cases.
- Guardrails, tool permissions and human approval for consequential actions.
- Monitoring, usage budgets and a process for approving changes.
Evaluation evidence informs the authority you approve; it does not prove that unexpected behavior is impossible. unicrew holds ISO 27001:2022 and ISO 9001:2015 certifications, renewed through a multi-stage audit with Quay Audit UK. These are organizational credentials.
Our internal agent proof of concept used AWS Bedrock, LangChain and Python for a browser-based workflow. For a commissioned build, we select the stack against the task, tool connections and deployment requirements.
Candidate models, including Anthropic Claude, need evaluation for task quality, tool behavior, latency, data terms and cost. A provider or model change can require adaptation and retesting; the stack is chosen for the workflow it needs to support.
Compare documented task scope, acceptance evidence and operating responsibility.
- Ask how tool access, consequential actions and human approval will be tested.
- Agree the owner of model usage, maintenance and future changes.
- Distinguish internal proofs of concept, owned products and client delivery when assessing examples.
unicrew has 100+ senior in-house engineers across six countries; the project managers and engineers a client works with are based in Ukraine, Poland, Estonia and the UK. Our unified Clutch display is 5.0, with 61 verified client reviews.
We built an internal browser-based agent as a proof of concept for finding missing time logs and sending notifications across unicrew's own team. Timely work-time logging compliance improved 30% in that internal trial.
Separately, our client AI work includes production search features, tagging and embeddings preparing vector search. Snaplore and Talkmetry are AI products we own and operate, both revenue-generating; Snaplore has active users. These are distinct kinds of engineering experience, with the internal trial providing the agent example.
An agent fits a defined job where the next action depends on varied inputs and tool feedback, and where you can specify acceptable results and authority.
Compare it with a scripted workflow, a user-directed assistant or your platform's packaged tools. Include data engineering where the task needs better data access or preparation. A pilot tests task performance and action boundaries; your responsible owner uses that evidence to decide the scope of a production build.