Should we use OpenAI, Claude, or an open model?

It depends on the task, your data-residency requirements and your cost profile. Some workloads are cheaper and better on a smaller model, some need a frontier model, and some should not use a model at all. We benchmark against your actual inputs before committing, and we build so the provider can be swapped without a rewrite.

How do you control AI costs?

By routing work to the smallest model that handles it, caching repeated context, capping token spend per operation, and logging usage per feature so cost is attributable. Most runaway AI bills come from sending far more context than the task needs on every single call.

Can you integrate AI with our existing CRM and internal tools?

Yes. Most work we do is exactly that: connecting a model to a CRM, help desk, database or internal API through a documented integration layer, so the AI reads and writes the same records your team does.

What about data privacy?

We scope what data leaves your systems before writing any code, strip or tokenise what does not need to be sent, and use providers and settings that exclude your data from model training. Where requirements are strict, self-hosted open models are a viable path and we will price both options.

AI Integration

AI Integration Services: Models Wired Into Your Real Systems

Vedixx connects AI models to the software you already run — CRM, help desk, databases, internal APIs — so they act on live data instead of sitting in a separate chat window. We handle authentication, context design, output validation, cost control and monitoring. The model is the easy part; the integration is the work.

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What is AI integration?

AI integration is the process of connecting an artificial intelligence model to a business’s existing software so it can read and write real data rather than operating in isolation. It covers API authentication, deciding what context to send the model on each request, validating the output before it reaches a system of record, controlling token cost, and monitoring accuracy over time.

What's included

Model selection & benchmarking

We test candidate models against your actual inputs with a scored rubric. Some workloads run better and far cheaper on a small model; some need a frontier one; some should not use a model at all.

Provider integration

OpenAI, Anthropic or open-weight models wired in behind an abstraction layer, so changing model or vendor later is a configuration change rather than a rewrite.

Context & retrieval architecture

Deciding what each call actually needs to see, and retrieving only that. This is where both accuracy and cost are genuinely won or lost.

Output validation

Schema-checked responses with retries, repair passes and fallbacks, so malformed or hallucinated output never reaches your database or your customer.

Cost controls

Per-operation token caps, caching of repeated context, model routing by task difficulty, and usage logged per feature so spend is attributable rather than one unexplained monthly bill.

Evaluation & monitoring

A test set of real inputs with expected outputs, run against changes, so you find out a prompt or model update broke something before your customers do.

What an integration layer actually does

Calling a model API is ten lines of code. Everything either side of that call is the part that determines whether the feature is safe to run unattended.

1
Request
A user action or system event needs something a model can do: classify, extract, summarise, draft.
2
Assemble context
Retrieve exactly the records and documents this task requires. Sending everything on every call is the most common cause of runaway AI bills.
3
Route to a model
Pick the cheapest model that handles this task reliably. Not every request needs the frontier model.
4
Validate output
Check the response against an expected schema and business rules. Retry or repair on failure; never write unvalidated model output.
5
Write & log
Commit to the system of record, and log input, output, latency and cost so quality and spend stay attributable per feature.

Where it pays for itself

Enquiry classification into the CRM

The problem: Inbound messages arrive as free text and someone reads each one to decide the product area, urgency and owner before it can be routed.
What we build: A classification step that reads the message, returns a validated structured record — category, urgency, suggested owner, summary — and writes it straight to the CRM.
The result: Routing happens on arrival, and the CRM holds structured fields instead of a wall of pasted text.

Document data extraction

The problem: Invoices, contracts and forms arrive as PDFs and get re-typed into internal systems, with the transcription errors that inevitably follow.
What we build: Extraction into a defined schema, validated against expected ranges and existing records, with clean matches written automatically and anything failing a check sent to a review queue.
The result: Most documents process untouched, and the exceptions arrive flagged with the reason they failed.

Call and meeting summarisation

The problem: Notes are written inconsistently or not at all, so the CRM record after a call reflects whoever remembered to type something.
What we build: Transcripts summarised into a fixed structure — decisions, objections, next steps, owner — then attached to the correct record automatically.
The result: Every conversation leaves a consistent, searchable trace without anyone writing it up.

Internal search across scattered systems

The problem: Answers live across a wiki, a shared drive, past tickets and Slack history, and finding anything depends on knowing where to look.
What we build: A retrieval layer indexing those sources with permission awareness, answering natural-language questions and citing the source document.
The result: Staff find answers directly instead of interrupting whoever wrote the original document.

How it works

1
Define the job and what correct means
What the model is for, what a good output looks like, and how it will be measured. Vague AI projects fail here, not in the code.
2
Benchmark on your data
Candidate models and prompts scored against real inputs, so selection is evidence rather than preference or whichever provider is in the news.
3
Design the context strategy
What gets retrieved and sent per call, and what does not. This decision sets both your accuracy ceiling and your monthly bill.
4
Build the integration layer
Auth, retries, schema validation, error handling, rate-limit backoff and logging — the parts that turn a demo into something safe to run unattended.
5
Instrument and hand over
Cost, latency and quality monitored per feature, with an evaluation set and documentation covering how to change prompts and swap providers.

Typical timeline

Phase	Duration	What you get
Discovery & scoping	3–5 days	Defined task, success criteria, data-handling boundaries and an evaluation approach.
Benchmarking	1 week	Scored comparison of candidate models and prompts against your real inputs, with a cost projection.
Integration build	2–5 weeks	Production integration with validation, retries, cost caps and structured logging.
Evaluation & hardening	1–2 weeks	Regression test set, failure-mode handling and monitoring dashboards.
Handover	Ongoing	Documentation, runbooks and optional retained support as models and prompts evolve.

Hosted API vs self-hosted open model

The right answer depends on your data obligations and volume, not on ideology. We price both when the choice is genuinely open, and we will tell you when it is not.

	Hosted API (OpenAI, Anthropic)	Self-hosted open model
Time to production	Fastest. No infrastructure to manage.	Slower. Requires provisioning, serving and scaling.
Cost shape	Per-token. Cheap at low volume, grows linearly.	Fixed infrastructure cost. Better economics at sustained high volume.
Data residency	Data leaves your infrastructure. Training exclusion is configurable.	Nothing leaves your environment. Suits strict obligations.
Capability ceiling	Highest available on the hardest reasoning tasks.	Strong and improving; excellent for classification and extraction.
Maintenance burden	Provider handles updates; you handle version changes.	You own serving, scaling, upgrades and uptime.

Technologies we build on

Model providers

OpenAI (GPT)
Anthropic (Claude)
Open-weight models
Embedding models

Integration

REST & GraphQL APIs
Webhooks
Queues & background jobs
OAuth & service auth

Data & retrieval

PostgreSQL
Vector search
Supabase
Caching layers

Reliability

Schema validation
Evaluation harnesses
Cost & usage logging
Alerting

Systems we connect

HubSpotSalesforcePipedriveZendeskIntercomSlackNotionAirtableStripeShopifyGoogle WorkspaceInternal REST APIs

Who this is for

SaaS & technology

AI features inside an existing product, built to the same reliability standard as the rest of the codebase.

Professional services

Document review, proposal drafting and meeting summarisation attached to the right client record.

Finance & accounting

Invoice and statement extraction with validation, where a wrong number is worse than no number.

E-commerce

Product data enrichment, review analysis and support classification at catalogue scale.

Logistics

Parsing unstructured supplier and carrier communication into structured status updates.

Agencies

Internal tooling that removes the research and summarisation load from delivery teams.

What drives the cost

Integration cost splits into build and running cost, and they pull in opposite directions: time spent on context design and model routing up front is what keeps the monthly bill from growing with your usage.

Task complexity

Classification into five categories is straightforward. Multi-step reasoning over long documents needs more design, more evaluation and more iteration.

Accuracy requirements

A first-draft assistant a human reviews needs far less hardening than a step that writes to a system of record unattended.

Volume

Token cost scales with usage. High volume justifies caching, smaller models on easy paths, and possibly self-hosting.

Number of systems

Each system the model reads from or writes to adds authentication, field mapping, rate limits and its own failure modes.

Data-handling obligations

Residency requirements or a rule that nothing leaves your infrastructure point toward self-hosted models, changing both build and running economics.

Evaluation depth

A proper regression test set costs real time to build and is the reason a model or prompt change does not silently break the feature six months later.

Feasibility sprint

Benchmark the task against real data and get a cost projection before committing to a build.

Integration project

Production build with validation, monitoring and documentation, scoped after the sprint.

Retained support

Model landscape and prompts move quickly. Suits teams running AI in a critical path.

What you end up with

AI acting on live records, not copy-pasted context
Predictable, attributable AI spend per feature
Validated output that cannot corrupt downstream systems
Provider choice kept open rather than locked in
A regression test set that catches breakage before customers do
A clear, documented answer on what data leaves your systems

Key takeaways

The model API call is the trivial part. Context design, validation and monitoring are the actual engineering.
Most runaway AI bills come from sending far more context than the task needs on every single call.
Never write unvalidated model output to a system of record — schema-check it, retry, then fall back.
Benchmark on your own data before choosing a model. The best model for your task is often not the biggest one.
Without an evaluation set, you cannot tell whether a prompt or model change improved or broke the feature.

Related services

AI Automation

Automations that keep running after the engagement ends.

Workflow Automation

Multi-step processes that run without supervision.

Business Process Automation

End-to-end process redesign, then automation.

CRM Automation

Pipeline hygiene and routing that maintains itself.

Marketing Automation

Lifecycle campaigns triggered by real behaviour.

WhatsApp Automation

Conversational flows on the channel this market actually uses.

FAQ

Questions? Answered.

Connecting a model to your systems so it can act on your real data rather than sitting in a separate chat window. That means API integration, authentication, prompt and context design, output validation, cost controls, and monitoring. The model is the easy part; the integration is the work.

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