AI implementation
AI implementation for companies, from first use case to working system.
We help you find where AI is genuinely useful in your operations, then build it into the tools and processes your team already uses, with clear limits, tests and a person in the loop where it matters.
Where AI implementation helps
Good starting points are specific, frequent and costly when done by hand.
Documents someone has to read and retype
Invoices, forms, contracts or emails that arrive in different formats and need the same data extracted, checked and entered somewhere else.
Knowledge scattered across files and people
Policies, product details and past answers spread across folders, wikis and inboxes, so the same questions get answered again and again.
Requests that need sorting before anyone can act
Support tickets, leads or internal requests that must be classified, summarized or routed before the right person picks them up.
An AI pilot that never reached daily use
A promising prototype without data access, permissions, evaluation or an owner, so it stays a demo instead of becoming part of the work.
What we implement
AI as a component of a working system, connected to your data and measured against criteria you agree with.
Document processing
Extraction and validation of fields from PDFs, emails and scans, with confidence checks and a review queue for uncertain cases.
Knowledge search inside your tools
Search and answers over your own documents (retrieval-augmented generation) inside the tools your team already uses, with sources shown and access limited to what each user may see.
Customer-facing chatbots and AI agentsClassification, routing and summaries
Models that sort, tag and summarize incoming work and hand it to the right queue or person, with the reasoning visible.
Workflow automation and integrationsAI inside the software you already use
AI features added to your CRM, help desk, internal tool or portal through their APIs, instead of a separate app nobody opens.
Evaluation and monitoring
Test sets built from real examples, quality criteria agreed with you and checks that run before and after every change.
A practical AI roadmap
A short list of use cases ranked by value, effort and risk, so you know what to build first and what to leave alone.
Works with your systems and the models you choose
- Model providers
- Documents and knowledge
- Business systems
We choose a model per task, based on quality, cost, data-handling terms and where the data is processed. Hesvian Labs is not affiliated with these providers.
How a project runs
Most AI implementation projects start with a Clarity Sprint: we review the process, the data and the risks, and agree on what success looks like before anything is built.
01
Clarity Sprint
Project fee $4,500
An audit of how the work happens today and a decision-ready system plan.
02
System Delivery
From $20,000
The software, AI capabilities and integrations defined in the blueprint, delivered in clear milestones.
03
Continuous Evolution
From $2,000
Ongoing improvements with agreed capacity, once the system is live.
All prices in USD, including applicable taxes. Each stage is scoped and approved separately. Pricing and what each stage includes
What AI implementation can’t promise
AI makes mistakes
Language models can produce wrong or invented answers. We design for it with sources, confidence thresholds, human review and fallbacks, but the risk never disappears entirely.
Data quality sets the ceiling
If the underlying documents or data are incomplete or contradictory, results will be too. Part of the work is often improving the inputs.
Some decisions stay with people
We don’t automate decisions with legal or similarly significant effects on people without human review and a sound basis to do so.
Compliance is a shared responsibility
We build with privacy and security in mind and explain the trade-offs. The legal assessment of your specific use, for example under data protection or sector rules, stays with you and your advisers.
Example: invoice intake for a finance team
Illustrative scenario — not a client project
A finance team receives supplier invoices by email in several formats. Two people open each attachment, copy the fields into the accounting system and chase missing purchase order numbers.
- Map the current path of an invoice, its exceptions and who approves what.
- Extract supplier, amounts, dates and purchase order number from each attachment, and validate them against the supplier list.
- Send clear cases to the accounting system through its API and uncertain ones to a review queue.
- Measure accuracy on a set of past invoices before going live, and keep reviewing a sample afterwards.
The aim would be fewer manual steps and a visible review queue. The actual effect depends on volumes and data quality, which the Clarity Sprint would measure first.
Questions
Do we need our own AI model?
Usually not. Most business uses work well with existing models accessed through an API, combined with your data and rules. Training or fine-tuning a model only makes sense in specific cases, and we would explain why before proposing it.
Will our data be used to train AI models?
That depends on each provider’s terms and the type of account. We review those terms with you before any data is sent and choose options that keep your data out of model training when that matters.
How do we know it works?
We agree on criteria at the start, such as accuracy on real examples, time per case or error types, and test against a set of real cases before and after launch.
Is AI implementation worth it for a small or mid-sized company?
Often, when there is a specific, frequent task with clear inputs, such as reading documents or sorting requests. It is rarely worth it as a general initiative without one. The Clarity Sprint checks the volume, the data and the cost before you commit to building anything.
Can you work with our IT team?
Yes. Integrations, permissions and decisions are documented in writing, and the handoff includes code and instructions your team can maintain.
Start with one process your team repeats.
Describe a process that takes too much manual work. You’ll get a straight answer on what can be automated, what needs custom software and what is better left as it is.