[ Custom AI Agents Development ]
AI agents that take action, not just answer.
RAG systems, document AI, internal copilots, and agentic automation, built to run reliably in production with evaluation and monitoring from day one.
[ Capability ]
AI that retrieves, reasons, and acts on your data.
We build the systems behind real AI products: knowledge retrieval, document processing, task automation, and decision support. Each one is engineered with the production discipline most generic builds skip, with evaluation, traceability, and monitoring from day one.
- No evaluation
- Quality drifts with every change and nobody notices.
- No traceability
- A wrong answer ships with no way to trace its source.
- No monitoring
- Failures surface as user complaints, not alerts.
[ What we build ]
The broader AI toolkit.
| Capability | Examples |
|---|---|
| RAG systems | Knowledge assistants, policy search, legal and document Q&A. |
| Document AI | Extraction, classification, summarization, and generation. |
| Workflow agents | CRM updates, data collection, and internal task automation. |
| AI copilots | Staff-facing assistants for support, sales, and operations. |
| Evaluation systems | Quality scoring, hallucination checks, and traceability. |
| Agentic prototypes | MVPs, technical validation, and product experiments. |
[ When this service fits ]
Where this work makes sense.
Workflow automation
You need AI that acts on your systems, not just chats.
Document-heavy work
Your workflows depend on documents or knowledge.
A staff-facing copilot
Your team needs an assistant inside their tools.
Demo to real system
You need to turn a prototype into a reliable system.
Reasoning over data
Decisions depend on synthesizing scattered information.
Trustworthy output
You need evaluation and traceability, not a black box.
[ What we engineer ]
The reliability layer around the model.
- Retrieval grounded in your own data
- Document processing and extraction pipelines
- Agentic workflows that take action, not just answer
- Integration with CRM, ticketing, and internal systems
- Human-in-the-loop review where it matters
- Quality scoring and hallucination checks
- Observability, traceability, and monitoring
[ In production ]
Agents we've taken live.
| Agent | What it does |
|---|---|
| Knowledge assistant | Answers from your docs with synthesis, not link lists. Read case |
| Document generator | Turns inputs into compliant, structured documents. Read case |
| Career / advisory copilot | Guides users through a domain-specific workflow. Read case |
| Sales context agent | Surfaces the right account context inside the CRM. Read case |
[ Paired with voice ]
The layer that makes a voice agent useful.
Most production voice agents lean on a deeper AI layer: the retrieval, documents, and automation behind the call.
| Voice agent | The AI layer behind it |
|---|---|
| Legal intake agent | Document intake and case-summary generation. Read case |
| Hotel concierge agent | Booking automation and guest-context management. Read case |
| Support voice agent | Knowledge-base RAG and ticket automation. Read case |
| Sales voice agent | CRM automation and lead scoring. Read case |
[ How a build runs ]
From workflow to production system.
-
1–2 weeks
Discover
Understand the workflow, users, data, and business goal.
-
1–2 weeks
Design
Define the AI logic, data sources, architecture, and success criteria.
-
2–4 weeks
Prototype
A controlled version for validation on real data.
-
1–2 weeks
Evaluate
Test quality, reliability, edge cases, and business usefulness.
-
1–2 weeks
Integrate
Connect the system to your tools and workflows.
-
1 week
Deploy
Launch with monitoring, documentation, and an improvement process.
[ Proven in production ]
AI agents we've shipped.
[ Common questions ]
Asked before most projects.
| Question | Answer |
|---|---|
| How is this different from a generic AI assistant? | These agents are grounded in your own data, integrated with your systems, and evaluated for reliability, not a generic model behind a chat box. |
| What kinds of agents can you build? | Knowledge assistants, document generators, workflow agents, staff copilots, and evaluation systems. |
| Can you build internal assistants for our team? | Yes, staff-facing copilots inside the tools your team already uses. |
| Can you work with our existing data and tools? | Yes. Grounding in your own data and integrating your systems is the core of the work. |
| How do you evaluate quality before launch? | Scoring against a rubric, hallucination checks, and traceability, measured before and after every change. |
6 yrs
in complex B2B software
20+
experts across AI, product, design, and engineering
4.9/5
average client satisfaction
5+
industries: SaaS, hospitality, LegalTech, MarTech, support
"What truly stood out was Softcery's deep AI expertise. They were able to take our vision and turn it into a reality, and the final product has exceeded our expectations. Working with Softcery has been a game-changer for our business."
Jeanette Kreft
Managing Director, The Compliance Company & Upskill AI
"Softcery is not your typical software development agency – they're a full-scale product consultancy. The benefit of working with them is the collaboration."
Ryan Tabb
Founder, Bullseye