AI for your customers
Customer-facing IT: let customers query your data directly. A support question on delivery times? Route the recurring cases through an agentic system (Support 3.0, now).
AI for your processes
We audit your business or a specific function to give you an outside view of your ambitions and what is actually possible. A moment to move forward on what agentic AI means and how it reshapes business models.
Today, desk AI looks too generic and too risky. Slow adoption and « shadow AI » reflect poor visibility and industry-wide hesitation.
Consumer « frontier » models are the most capable, but risks are many : dependence, loss of control, dilution of domain expertise, price drift, no confidentiality.
Hosted open-source models offer confidentiality and cost control, with performance that is largely sufficient for most use cases in practice.
Adapting processes
The old change-management line ! Often sold, rarely delivered. This technological shift does seem to demand real executive reflection this time.
Keep your SaaS stack or replace it? The model of UI-heavy systems with operators trained by consultancies is due for reinvention. What replaces it is more direct data access for the agents you deploy, and a modern interface (supervision-first, conversational, or invisible). Retiring part of the legacy stack is increasingly discussed, even when it stings. Successful vendors will bolt on AI to keep you, but cross-cutting data access unlocks deeper change.
Two benefits : accelerate or transform. Real change usually means clarifying human responsibility, delegating to agents when performance holds, and human intervention in carefully defined cases. A « human-in-the-loop » capability is sometimes useful, temporary or permanent and reversible.
“The most promising companies are those tackling the hard work of re-architecturing systems around LLMs, building robust agentic systems.”
Rick Sherlund, Founder and CEO, Sherlund Partners, for Goldman Sachs (March 2026)
Our studio can implement best practice on intent, regulation, security, and performance,
Audit, process review... we challenge you on accelerations or transformations to consider.
The Use cases tab gathers recent reports listing many examples of agentic AI in industry.
Among them, bespoke agentic applications could look like :
- complex or high-value report generation (e.g. OSINT, competitive intelligence),
- BI, data science tooling, advanced dashboards,
- a conversational « excel killer » built on tabular data,
- conversational systems for customers or staff that finally query your data,
- trading, computation, and machine learning systems,
- systems with real-time or safety-critical constraints,
- opening your site or inventory to consumer or partner AIs (MCP servers).
Control
With a studio, metrics and goals were defined carefully. They may evolve. You have partners who place control exactly where you expect.
Requirements and design should inspire confidence, meaning in practice verifiable control. Your expertise sits at the centre. We call these business decision milestones or critical reasoning checkpoints. But that must be visible : interfaces must serve this goal with maximum fluidity.
You may also need explainability. Integrate explanatory outputs to trace reasoning steps in each inference. Anyone can then refine judgement on system performance from the UI and grant the trust they choose, knowingly.
Logging and log analysis can support auditability and surface unwanted drift immediately.
But also... control how AI arrives with professionals who free staff from work that is not their job.
Spare yourself the heavy task of spending hours evaluating and communicating on the quality of a generic system’s outputs, discovering it, onboarding. Adapting to a feature that is finally opaque, not directly usable under your methods, and replaced after a while by a new version ?
That undermines prior work. A core benefit of bespoke is building a system that fits you and evolves at your pace.
Claude’s « skills » are a good example : trivial for an AI engineer who knows they are still prompts, but once saved, social media shows the huge comment volume on the attention these tools demand, even on details.
Incumbents’ bet is to enter the enterprise at the price of colossal, permanent adaptation work that poses cognitive problems, beyond cost and confidentiality.
Those firms benefit from an interested attitude among many people in tech, not truly AI specialists but seeking a smart career pivot, all helping legitimise shadow AI as profitable coaching ground : an ecosystem forms around prompting, a consumer approach but lower performance, while AI is also, now, pure players integrating it as more advanced programmes for their clients... turnkey, for a fraction of the price !
Keeping professional skills alive
This is an example often cited for bespoke superiority over consumer tools : visible milestones across the AI system, through its interface (its design).
A dedicated product can carry your good practice through design itself, becoming a medium for exchange, teaching, or team training.
Intent can be made visible. Seniors are reassured by milestones in place; juniors can audit system outputs, verify them, and learn from meaningful sequences.
If practices differ from one company to another, which desk AI could present information correctly against each company’s practice ?
Flow
Productivity gains are generally expected from AI systems, through automating tasks AI can handle.
Two near-term shifts for staff :
- better workstation ergonomics with conversational tools to query enterprise data and act by voice,
- repositioning work toward edge cases, system supervision, and takeover when needed.
Also…
The Market fit tab covers regulation (and how our systems expertise exceeds expectations) and adoption friction. In Reports, you will find recent institutional analyses on the agentic revolution that shaped our studio positioning.
See also
AI in your product
Software vendor (SaaS, production systems, mission-critical)? You want agents in-product but keep stalling? We ship bespoke.